Search

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

Background: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. Methods: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. Results: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. Conclusions: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) typically uses wearable sensors to identify and analyze the time-series data they collect, enabling recognition of specific actions. As such, HAR is increasingly applied in human–computer interaction, healthcare, and other fields, making accurate and efficient recognition of various human activities. In recent years, deep learning methods have been extensively applied in sensor-based HAR, yielding remarkable results. However, complex HAR research, which involves specific human behaviors in varied contexts, still faces several challenges. To solve these problems, we propose a multi-head neural network based on the attention mechanism. This framework contains three convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The model uses a channel attention module (squeeze–excitation module) to enhance the representational capabilities of convolutional neural networks. We conducted experiments on two publicly available benchmark datasets, UCI-HAR and WISDM, to evaluate our model. The results were satisfactory, with overall recognition accuracies of 96.72% and 97.73% on their respective datasets. The experimental results demonstrate the effectiveness of the network structure for the HAR, which ensures a higher level of accuracy. [ABSTRACT FROM AUTHOR]

With the development of the ageing society, wearable strain sensors have great application prospects in the fields of health detection, artificial intelligence, and motion recognition. However, traditional forms of film‐like strain sensors suffer from low sensitivity, adhesion, and impermeability during use, which severely limit their practical applications. To overcome these problems, we propose a strategy to fabricate polyurethane/polydopamine/MXene (TPU/PDA/MXene) composite fibers by wet spinning. Furthermore, the outside is wrapped with (PDMS), giving mechanical strength and chemical resistance. Through a simple and low‐cost wet spinning process, TPU/PDA/MXene fibers are achieved in the preparation of flexible strain sensors, and they can be woven into various forms with good flexibility and comfort according to practical needs. The results indicated that the prepared flexible strain sensor exhibits a high gauge factor (GF) of 57.15 over a large strain range (300 %), which are superior to those of many fiber‐based sensors. Their application properties were also measured by attaching them to fingers, wrists and knees, showing high sensitivity, mechanical durability and chemical stability, and has great potential for wearable strain sensors and flexible strain sensors in complex environments. [ABSTRACT FROM AUTHOR]

Sensor-based human activity recognition has been widely used in various fields; however, there are still challenges involving recognition of daily complex human activities using sensors. In order to solve the problem of timeliness and homogeneity of recognition functions in human activity recognition models, we propose a human activity recognition model called 'BSTCA-HAR' based on a long short-term memory (LSTM) network. The approach proposed in this paper combines an attention mechanism and a temporal convolutional network (TCN). The learning and prediction units in the model can efficiently learn important action data while capturing long time-dependent information as well as features at different time scales. Our series of experiments on three public datasets (WISDM, UCI-HAR, and ISLD) with different data features confirm the feasibility of the proposed method. This method excels in dynamically capturing action features while maintaining a low number of parameters and achieving a remarkable average accuracy of 93 % , proving that the model has good recognition performance. [ABSTRACT FROM AUTHOR]

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field. [ABSTRACT FROM AUTHOR]

In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]

Background: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. Methods: A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. Results: A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. Conclusions: The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods. [ABSTRACT FROM AUTHOR]

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa−1 from 0 to 30 kPa) and wide working pressure range (0–200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human–machine interaction. [ABSTRACT FROM AUTHOR]

Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier's body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport. [ABSTRACT FROM AUTHOR]

Sarcopenia recognition is very crucial in the early diagnosis of sarcopenia. However, the commonly used screening methods are limited by real‐time property, portability, and convenient usability at home. Herein, an electrospun BaTiO3 film is proposed and a piezoelectric sensor with silver electrodes and polyimide substrates is fabricated. The sensor exhibits high piezoelectricity (74.2 pC N−1), sensitivity, linearity, low detection limit (0.2 mN), and significant bending ability (bending angle can exceed 90°), maintaining stable output after more than 20 000 cycles during a week. Due to its excellent performance, the piezoelectric sensor to the recognition of sarcopenia is applied and a wearable system to collect piezoelectric signals from the lower limb movements of the elderly is developed. By selecting features from these signals, eight kinds of machine learning models are employed and their performances in recognizing sarcopenia are compared in both male and female groups. The results indicate that the artificial neural network (ANN) model has the highest performance, with accuracies of 92.9% in males and 98.1% in females. This piezoelectric sensor, combined with a wireless communication module, is expected to provide crucial evidence for sarcopenia detection, offering a new, convenient, and household screening solution for early diagnosis and prevention of sarcopenia. [ABSTRACT FROM AUTHOR]

Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset. [ABSTRACT FROM AUTHOR]

Conventional sensors can detect the various physical and chemical changes that occur during deformation and the states of materials with a high accuracy. However, almost all existing sensors are bulky and stiff, making them highly susceptible to failure when used in large deformation-compliant structures. By contrast, despite their relatively-low resolution and accuracy, the demand for flexible and stretchable sensors used in detecting human motion and vital signs has drastically increased. There are several types of stretchable sensors that contain functional materials such as carbon nanotubes (CNTs). These stretchable sensors have different working principles and applications based on the attributes of their core components. Several electrical signals are used in the measurement of the different physical parameters such as the electrical resistance and capacitance. Triboelectric nanogeneration or voltage signals that can be identified under contact or impact loading conditions are employed in some measurement methods. Additionally, an array of microstructures with a special shape that is directly related to movement and deformation detection, as well as conductive nano-particles that are elaborately aligned along a certain direction for specific sensor applications, are used in the other methods. In this study, a review of the recently-developed high-performance stretchable sensors made from functional materials using new sensing mechanisms for wearable robot applications is provided. [ABSTRACT FROM AUTHOR]

Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature has presented a multitude of deep learning models that aim to derive a suitable feature representation from temporal sensory input. However, the presence of a substantial quantity of annotated training data is crucial to adequately train the deep networks. Nevertheless, the data originating from the wearable devices are vast but ineffective due to a lack of labels which hinders our ability to train the models with optimal efficiency. This phenomenon leads to the model experiencing overfitting. The contribution of the proposed research is twofold: firstly, it involves a systematic evaluation of fifteen different augmentation strategies to solve the inadequacy problem of labeled data which plays a critical role in the classification tasks. Secondly, it introduces an automatic feature-learning technique proposing a Multi-Branch Hybrid Conv-LSTM network to classify human activities of daily living using multimodal data of different wearable smart devices. The objective of this study is to introduce an ensemble deep model that effectively captures intricate patterns and interdependencies within temporal data. The term "ensemble model" pertains to fusion of distinct deep models, with the objective of leveraging their own strengths and capabilities to develop a solution that is more robust and efficient. A comprehensive assessment of ensemble models is conducted using data-augmentation techniques on two prominent benchmark datasets: CogAge and UniMiB-SHAR. The proposed network employs a range of data-augmentation methods to improve the accuracy of atomic and composite activities. This results in a 5% increase in accuracy for composite activities and a 30% increase for atomic activities. [ABSTRACT FROM AUTHOR]

Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios. [ABSTRACT FROM AUTHOR]

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation. [ABSTRACT FROM AUTHOR]

Sweat is an accessible biofluid that provides useful physiological information about the body's biomolecular state and systemic health. Wearable sensors possess various advantageous features, such as lightweight design, wireless connectivity, and compatibility with human skin, that make them suitable for continuous monitoring. Wearable electrochemical sweat sensors can diagnose diseases and monitor health conditions by detecting biomedical signal changes in sweat. This paper discusses the state-of-the-art research in the field of wearable sweat sensors and the materials used in their construction. It covers biomarkers present in sweat, sensing modalities, techniques for sweat collection, and ways to power these sensors. Innovative materials are categorized into three subcategories: sweat collection, sweat detection, and self-powering. These include substrates for sensor fabrication, analyte detection electrodes, absorbent patches, microfluidic devices, and self-powered devices. This paper concludes by forecasting future research trends and prospects in material-based wearable non-invasive sweat sensors. [ABSTRACT FROM AUTHOR]

The circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time series data collected on the same individuals due to the development of new data collection methods such as smartphone applications and wearable sensors. Estimating the circumplex model with time series data is more complex than with cross-sectional data because scores at nearby time points tend to be correlated. We adapt Browne's circumplex model to accommodate time series data. We illustrate the proposed method with an empirical data set of daily affect ratings of an individual over 70 days. We conducted a simulation study to explore the statistical properties of the proposed method. The results show that the method provides more satisfactory confidence intervals and test statistics than a method that treats time series data as if they were cross-sectional data. [ABSTRACT FROM AUTHOR]

Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model's efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation. [ABSTRACT FROM AUTHOR]

Portable devices like accelerometers and physiological trackers capture movement and biometric data relevant to sports. This study uses data from wearable sensors to investigate deep learning techniques for recognizing human behaviors associated with sports and fitness. The proposed CNN-BiGRU-CBAM model, a unique hybrid architecture, combines convolutional neural networks (CNNs), bidirectional gated recurrent unit networks (BiGRUs), and convolutional block attention modules (CBAMs) for accurate activity recognition. CNN layers extract spatial patterns, BiGRU captures temporal context, and CBAM focuses on informative BiGRU features, enabling precise activity pattern identification. The novelty lies in seamlessly integrating these components to learn spatial and temporal relationships, prioritizing significant features for activity detection. The model and baseline deep learning models were trained on the UCI-DSA dataset, evaluating with 5-fold cross-validation, including multi-class classification accuracy, precision, recall, and F1-score. The CNN-BiGRU-CBAM model outperformed baseline models like CNN, LSTM, BiLSTM, GRU, and BiGRU, achieving state-of-the-art results with 99.10% accuracy and F1-score across all activity classes. This breakthrough enables accurate identification of sports and everyday activities using simplified wearables and advanced deep learning techniques, facilitating athlete monitoring, technique feedback, and injury risk detection. The proposed model's design and thorough evaluation significantly advance human activity recognition for sports and fitness. [ABSTRACT FROM AUTHOR]

In recent years, researchers have successfully recognised human activities using commercially available WiFi (Wireless Fidelity) devices. The channel state information (CSI) can be gathered at the access point with the help of a network interface controller (NIC card). These CSI streams are sensitive to human body motions and produce abrupt changes (fluctuations) in their magnitude and phase values when a moving object interacts with a transmitter and receiver pair. This sensing methodology is gaining popularity compared to traditional approaches involving wearable technology, as it is a contactless sensing strategy with no cumbersome sensing equipments fitted on the target with preserved privacy since no personal information of the subject is collected. In previous investigations, internal validation statistics have been promising. However, external validation results have been poor, due to model application to varying subjects with remarkably different environments. To address this problem, we propose an adversarial Artificial Intelligence AI model that learns and utilises domain-invariant features. We analyse model results in terms of suitability for inter-domain and intra-domain alignment techniques, to identify which is better at robustly matching the source to target domain, and hence improve recognition accuracy in cross-user conditions for HAR using wireless signals. We evaluate our model performance on different target training data percentages to assess model reliability on data scarcity. After extensive evaluation, our architecture shows improved predictive performance across target training data proportions when compared to a non-adversarial model for nine cross-user conditions with comparatively less simulation time. We conclude that inter-domain alignment is preferable for HAR applications using wireless signals, and confirm that the dataset used is suitable for investigations of this type. Our architecture can form the basis of future studies using other datasets and/or investigating combined cross-environmental and cross-user features. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their Activities of Daily Living (ADL) by analyzing data collected from various sensors. Apart from wearable sensors, the adoption of camera frames to analyze and classify ADL has emerged as a promising trend for achieving the identification and classification of ADL. To accomplish this, the existing approaches typically rely on object classification with pose estimation using the image frames collected from cameras. Given the existence of inherent correlations between human–object interactions and ADL, further efforts are often needed to leverage these correlations for more effective and well justified decisions. To this end, this work proposes a framework where Graph Neural Networks (GNN) are adopted to explicitly analyze human–object interactions for more effectively recognizing daily activities. By automatically encoding the correlations among various interactions detected through some collected relational data, the framework infers the existence of different activities alongside their corresponding environmental objects. As a case study, we use the Toyota Smart Home dataset to evaluate the proposed framework. Compared with conventional feed-forward neural networks, the results demonstrate significantly superior performance in identifying ADL, allowing for the classification of different daily activities with an accuracy of 0.88. Furthermore, the incorporation of encoded information from relational data enhances object-inference performance compared to the GNN without joint prediction, increasing accuracy from 0.71 to 0.77. [ABSTRACT FROM AUTHOR]

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process. [ABSTRACT FROM AUTHOR]

Featured Application: This study protocol is relevant as it serves as a guideline for describing the steps to follow for the creation of a multisensory system that is capable of automatically analyzing human activity and determining their functional ability and state of dependence. It also contains valuable information on the clinical validation process of the system. The EYEFUL system represents a pioneering initiative designed to leverage multisensory systems for the automatic evaluation of functional ability and determination of dependency status in people performing activities of daily living. This interdisciplinary effort, bridging the gap between engineering and health sciences, aims to overcome the limitations of current evaluation tools, which often lack objectivity and fail to capture the full range of functional capacity. Until now, it has been derived from subjective reports and observational methods. By integrating wearable sensors and environmental technologies, EYEFUL offers an innovative approach to quantitatively assess an individual's ability to perform activities of daily living, providing a more accurate and unbiased evaluation of functionality and personal independence. This paper describes the protocol planned for the development of the EYEFUL system, from the initial design of the methodology to the deployment of multisensory systems and the subsequent clinical validation process. The implications of this research are far-reaching, offering the potential to improve clinical evaluations of functional ability and ultimately improve the quality of life of people with varying levels of dependency. With its emphasis on technological innovation and interdisciplinary collaboration, the EYEFUL system sets a new standard for objective evaluation, highlighting the critical role of advanced screening technologies in addressing the challenges of modern healthcare. We expect that the publication of the protocol will help similar initiatives by providing a structured approach and rigorous validation process. [ABSTRACT FROM AUTHOR]

Wearable sensors are widely used in medical applications and human–computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher. [ABSTRACT FROM AUTHOR]

In recent years, the development of sensor and wearable technologies have led to their increased adoption in clinical and health monitoring settings. One area that is in early, but promising, stages of development is the use of biosensors for therapeutic drug monitoring (TDM). Traditionally, TDM could only be performed in certified laboratories and was used in specific scenarios to optimize drug dosage based on measurement of plasma/blood drug concentrations. Although TDM has been typically pursued in settings involving medications that are challenging to manage, the basic approach is useful for characterizing drug activity. TDM is based on the idea that there is likely a clear relationship between plasma/blood drug concentration (or concentration in other matrices) and clinical efficacy. However, these relationships may vary across individuals and may be affected by genetic factors, comorbidities, lifestyle, and diet. TDM technologies will be valuable for enabling precision medicine strategies to determine the clinical efficacy of drugs in individuals, as well as optimizing personalized dosing, especially since therapeutic windows may vary interindividually. In this mini-review, we discuss emerging TDM technologies and their applications, and factors that influence TDM including drug interactions, polypharmacy, and supplement use. We also discuss how using TDM within single subject (N-of-1) and aggregated N-of-1 clinical trial designs provides opportunities to better capture drug response and activity at the individual level. Individualized TDM solutions have the potential to help optimize treatment selection and dosing regimens so that the right drug and right dose may be matched to the right person and in the right context. [ABSTRACT FROM AUTHOR]

Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightweight nature and simplicity. Recently, HAR techniques leveraging wearable biometric signals, such as electrocardiography (ECG) and photoplethysmography (PPG), have been proposed using publicly available datasets. However, to facilitate broader practical applications, a more extensive analysis based on larger databases with cross-subject validation is required. In pursuit of this objective, we initially gathered PPG signals from 40 participants engaged in five common daily activities. Subsequently, we evaluated the feasibility of classifying these activities using deep learning architecture. The model's performance was assessed in terms of accuracy, precision, recall, and F-1 measure via cross-subject cross-validation (CV). The proposed method successfully distinguished the five activities considered, with an average test accuracy of 95.14%. Furthermore, we recommend an optimal window size based on a comprehensive evaluation of performance relative to the input signal length. These findings confirm the potential for practical HAR applications based on PPG and indicate its prospective extension to various domains, such as healthcare or fitness applications, by concurrently analyzing behavioral and health data through a single biometric signal. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) stands at the intersection of machine learning, deep learning, and sensor technology, primarily focusing on leveraging inertial sensors in smartphones and wearable devices. This paper presents a comprehensive technical overview of HAR, examining the amalgamation of machine learning and deep learning systems while considering the data inputs from mobile and wearable inertial sensors. The review encompasses a broad spectrum of methodologies applied to HAR, ranging from classical machine learning algorithms to cutting-edge deep learning architectures. Emphasis is placed on the nuanced challenges and opportunities posed using inertial sensors in smartphones and wearables. This includes discussions on data preprocessing strategies, feature extraction methods, and model architectures, accounting for the unique characteristics of sensor data, such as noise, variability, and power consumption. The paper explores recent advancements, scrutinizing state-of-theart approaches, innovative model architectures, and emerging trends in HAR. Through a comparative evaluation of various machine learning and deep learning techniques, the review aims to guide researchers and practitioners in selecting the most appropriate methods for HAR applications across diverse scenarios. In conclusion, this paper serves as an inclusive guide to the technical landscape of HAR, incorporating insights from both mobile and wearable inertial sensors. By synthesizing existing knowledge and addressing future research directions, it aims to propel advancements in developing robust and efficient systems for recognizing human activities, accommodating the evolving landscape of sensor technologies in mobile and wearable devices. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) identifies people's motions and actions in daily life. HAR research has grown with the popularity of internet-connected, wearable sensors that capture human movement data to detect activities. Recent deep learning advances have enabled more HAR research and applications using data from wearable devices. However, prior HAR research often focused on a few sensor locations on the body. Recognizing real-world activities poses challenges when device positioning is uncontrolled or initial user training data are unavailable. This research analyzes the feasibility of deep learning models for both position-dependent and position-independent HAR. We introduce an advanced residual deep learning model called Att-ResBiGRU, which excels at accurate position-dependent HAR and delivers excellent performance for position-independent HAR. We evaluate this model using three public HAR datasets: Opportunity, PAMAP2, and REALWORLD16. Comparisons are made to previously published deep learning architectures for addressing HAR challenges. The proposed Att-ResBiGRU model outperforms existing techniques in accuracy, cross-entropy loss, and F1-score across all three datasets. We assess the model using k-fold cross-validation. The Att-ResBiGRU achieves F1-scores of 86.69%, 96.23%, and 96.44% on the PAMAP2, REALWORLD16, and Opportunity datasets, surpassing state-of-the-art models across all datasets. Our experiments and analysis demonstrate the exceptional performance of the Att-ResBiGRU model for HAR applications. [ABSTRACT FROM AUTHOR]

Batteryless sensors promise a sustainable future for sensing, but they face significant challenges when storing and using environmental energy. Incoming energy can fluctuate unpredictably between periods of scarcity and abundance, and device performance depends on both incoming energy and how much a device can store. Existing batteryless devices have used fixed or run-time selectable front-end capacitor banks to meet the energy needs of different tasks. Neither approach adapts well to rapidly changing energy harvesting conditions, nor does it allow devices to store excess energy during times of abundance without sacrificing performance. This article presents Stash, a hardware back-end energy storage technique that allows batteryless devices to charge quickly and store excess energy when it is abundant, extending their operating time and carrying out additional tasks without compromising the main ones. Stash performs like a small capacitor device when small capacitors excel and like a large capacitor device when large capacitors excel, with no additional software complexity and negligible power overhead. We evaluate Stash using two applications—temperature sensing and wearable activity monitoring—under both synthetic solar energy and recorded solar and thermal traces from various human activities. Our results show that Stash increased sensor coverage by up to 15% under variable energy-harvesting conditions when compared to competitor configurations that used fixed small, large, and reconfigurable front-end energy storage. [ABSTRACT FROM AUTHOR]

Minimally invasive microneedle (MN) is an emerging technology platform for wearable and implantable diagnostics and therapeutics systems. These short MNs offer pain-free insertion and simple operation. Among the MN technologies proposed to enhance interstitial fluid (ISF) extraction, porous and swellable (P-S) hydrogels absorb analyte molecules across the entire lateral surface. Currently, the design, development, and optimization of the MNs rely on empirical, iterative approaches. Based on theory of fluid flow and analyte diffusion through geometrically complex biomimetic systems, here we derive a generalized physics-guided model for P-S MN sensors. The framework (a) quantifies MN extracting efficiency η PS in terms of its geometric and physical properties, and (b) suggests strategies to optimize sensor response while satisfying the mechanical constraints related to various skin-types (e.g., mouse, pig, humans, etc.). Our results show that, despite the differences in geometry and composition, P-S MNs obey a universal scaling response, η PS ∼ ζ h T l n 2 D n s n with l n , D n , s being MN length, diffusivity, and radius, respectively, and ζ , h T and n are the ratio between approximate vs. exact analytical solutions, the effective biofluid transfer coefficient between dermis and skin, and the exponent for the power-law approximation, respectively. These parameters quantify the biomolecule transfer through the dermis-to-MN interface at different scaling limits. P-S MNs outperform hollow MNs by a 2-6x enhancement factor; however, the buckling-limit of insertion defines the maximized functionality of the sensor. Our model, validated against experimental results and numerical simulations, offers a predictive design framework to significantly reduce the optimization time for P-S MN-based sensor platforms. [ABSTRACT FROM AUTHOR]

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model's recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%. [ABSTRACT FROM AUTHOR]

Wearable devices have been widely used for the home monitoring of physical activities and healthcare conditions, among which ambulatory electrocardiogram (ECG) stands out for the diagnostic cardiovascular information it contains. Continuous and unobtrusive sensing often requires the integration of wearable sensors to existing devices such as watches, armband, headphones, etc.; nonetheless, it is difficult to detect high-quality ECG due to the nature of low signal amplitude at these areas. In this paper, a high-performance system with multi-channel signal superposition for weak ECG real-time detection is proposed. Firstly, theoretical analysis and simulation is performed to demonstrate the effectiveness of this system design. The detection system, including electrode array, acquisition board, and the application (APP), is then developed and the electrical characteristics are measured. A common mode rejection ratio (CMRR) of up to 100 dB and input inferred voltage noise below 1 μV are realized. Finally, the technique is implemented in form of ear-worn and armband devices, achieving an SNR over 20 dB. Results are also compared with the simultaneous recording of standard lead I ECG. The correlation between the heart rates derived from experimental and standard signals is higher than 0.99, showing the feasibility of the proposed technique. [ABSTRACT FROM AUTHOR]

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a "gold standard" and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) based on wearable sensors has emerged as a low-cost key-enabling technology for applications such as human–computer interaction and healthcare. In wearable sensor-based HAR, deep learning is desired for extracting human active features. Due to the spatiotemporal dynamic of human activity, a special deep learning network for recognizing the temporal continuous activities of humans is required to improve the recognition accuracy for supporting advanced HAR applications. To this end, a residual multifeature fusion shrinkage network (RMFSN) is proposed. The RMFSN is an improved residual network which consists of a multi-branch framework, a channel attention shrinkage block (CASB), and a classifier network. The special multi-branch framework utilizes a 1D-CNN, a lightweight temporal attention mechanism, and a multi-scale feature extraction method to capture diverse activity features via multiple branches. The CASB is proposed to automatically select key features from the diverse features for each activity, and the classifier network outputs the final recognition results. Experimental results have shown that the accuracy of the proposed RMFSN for the public datasets UCI-HAR, WISDM, and OPPORTUNITY are 98.13%, 98.35%, and 93.89%, respectively. In comparison with existing advanced methods, the proposed RMFSN could achieve higher accuracy while requiring fewer model parameters. [ABSTRACT FROM AUTHOR]

Advancements in sensing technology have expanded the capabilities of both wearable devices and smartphones, which are now commonly equipped with inertial sensors such as accelerometers and gyroscopes. Initially, these sensors were used for device feature advancement, but now, they can be used for a variety of applications. Human activity recognition (HAR) is an interesting research area that can be used for many applications like health monitoring, sports, fitness, medical purposes, etc. In this research, we designed an advanced system that recognizes different human locomotion and localization activities. The data were collected from raw sensors that contain noise. In the first step, we detail our noise removal process, which employs a Chebyshev type 1 filter to clean the raw sensor data, and then the signal is segmented by utilizing Hamming windows. After that, features were extracted for different sensors. To select the best feature for the system, the recursive feature elimination method was used. We then used SMOTE data augmentation techniques to solve the imbalanced nature of the Extrasensory dataset. Finally, the augmented and balanced data were sent to a long short-term memory (LSTM) deep learning classifier for classification. The datasets used in this research were Real-World Har, Real-Life Har, and Extrasensory. The presented system achieved 89% for Real-Life Har, 85% for Real-World Har, and 95% for the Extrasensory dataset. The proposed system outperforms the available state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors. [ABSTRACT FROM AUTHOR]

Flexible wearable sensors are integral in diverse applications, particularly in healthcare and human–computer interaction systems. This paper introduces a resistive stretch sensor crafted from shape memory polymers (SMP) blended with carbon nanotubes (CNTs) and coated with silver paste. Initially, the sensor's characteristics underwent evaluation using a Universal Testing Machine (UTM) and an LCR meter. These sensors showcased exceptional sensitivity, boasting a gauge factor of up to 20 at 5% strain, making them adept at detecting subtle movements or stimuli. Subsequently, the study conducted a comparison between SMP-CNT conductors with and without the silver coating layer. The durability of the sensors was validated through 1000 cycles of stretching at 4% ∆R/R0. Lastly, the sensors were utilized for monitoring respiration and measuring human breathing. Fourier transform and power spectrum density (PSD) analysis were employed to discern frequency components. Positioned between the chest and abdominal wall for contact-based respiration monitoring, the sensors revealed a dominant frequency of approximately 0.35 Hz. Signal filtering further enhanced their ability to capture respiration signals, establishing them as valuable tools for next-generation personalized healthcare applications. [ABSTRACT FROM AUTHOR]

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things and smart wearable devices, there is an increasing demand for motion capture and image recognition technology. This study aims to develop an optical motion capture system based on wearable devices of the Internet of Things to achieve accurate recognition and dynamic visual simulation of dance movements. This paper uses a motion capture technique based on optical principle. First, multiple optical sensors were installed on the wearable device to capture the dancers' movements. Then through the image recognition algorithm, the image is processed and analyzed to extract the dancers' posture and movement information. Finally, through the simulation algorithm, the captured dancers' movements are transformed into dynamic visualizations in real time. The experimental results show that the system can accurately capture and identify the movements of different dancers and provide high-quality dynamic visualizations. Through the system, dancers can observe their movements in real time, check and improve dance skills, and improve the quality of dance performance. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) aims to collect time series through wearable devices to precisely identify specific actions. However, the traditional HAR method ignores the activity variances among individuals, which will cause low generalization when applied to a new individual and indirectly enhance the difficulties of personalized HAR service. In this paper, we fully consider activity divergence among individuals to develop an end-to-end model, the multi-source unsupervised co-transfer network (MUCT), to provide personalized activity recognition for new individuals. We denote the collected data of different individuals as multiple domains and implement deep domain adaptation to align each pair of source and target domains. In addition, we propose a consistent filter that utilizes two heterogeneous classifiers to automatically select high-confidence instances from the target domain to jointly enhance the performance on the target task. The effectiveness and performance of our model are evaluated through comprehensive experiments on two activity recognition benchmarks and a private activity recognition data set (collected by our signal sensors), where our model outperforms traditional transfer learning methods at HAR. [ABSTRACT FROM AUTHOR]

A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users' profiles and can be used for real-time prediction in 391 milliseconds per request. [ABSTRACT FROM AUTHOR]

The inculcation of bioinspiration in sensing and human-machine interface (HMI) technologies can lead to distinctive characteristics such as conformability, low power consumption, high sensitivity, and unique properties like self-healing, self-cleaning, and adaptability. Both sensing and HMI are fields rife with opportunities for the application of bioinspired nanomaterials, particularly when it comes to wearable sensory systems where biocompatibility is an additional requirement. This review discusses recent development in bioinspired nanomaterials for wearable sensing and HMIs, with a specific focus on state-of-the-art bioinspired capacitive sensors, piezoresistive sensors, piezoelectric sensors, triboelectric sensors, magnetoelastic sensors, and electrochemical sensors. We also present a comprehensive overview of the challenges that have hindered the scientific advancement in academia and commercialization in the industry. [ABSTRACT FROM AUTHOR]

Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an inertial measurement unit (IMU) to measure the foot strike angle (FSA), and explores eight different kinematic parameters as potential indicators for forward propulsion. Twelve people with stroke performed walking trials while equipped with five IMUs and markers for optical motion analysis (the gold standard). The validity of the IMU-based FSA was assessed via Bland–Altman analysis, ICC, and the repeatability coefficient. Eight different kinematic parameters were compared to the forward propulsion via Pearson correlation. Analyses were performed on a stride-by-stride level and within-subject level. On a stride-by-stride level, the mean difference between the IMU-based FSA and OMCS-based FSA was 1.4 (95% confidence: −3.0; 5.9) degrees, with ICC = 0.97, and a repeatability coefficient of 5.3 degrees. The mean difference for the within-subject analysis was 1.5 (95% confidence: −1.0; 3.9) degrees, with a mean repeatability coefficient of 3.1 (SD: 2.0) degrees. Pearson's r value for all the studied parameters with forward propulsion were below 0.75 for the within-subject analysis, while on a stride-by-stride level the foot angle upon terminal contact and maximum foot angular velocity could be indicative for the peak forward propulsion. In conclusion, the FSA can accurately be assessed with an IMU on the foot in people with stroke during regular walking. However, no suitable kinematic indicator for forward propulsion was identified based on foot and shank movement that could be used for feedback in people with stroke. [ABSTRACT FROM AUTHOR]

Stretchable sensors that utilize machine‐assisted printing techniques afford significant benefits in terms of both bulk and output uniformities. These sensors have found extensive utility across diverse fields, such as wearables, robotics, and biomedical applications. However, existing printed sensor systems, once printed, are limited in their model variations and are heavily dependent on passive materials owing to their substrate‐bound nature. In this study, a printed sensor that utilizes a substrate–sensor isolation strategy is reported, thereby allowing size and design reconfigurability and a Lego‐like assembly. This approach enables device customization through post‐design modifications of specific sensing targets. The electrohydrodynamic printing process is utilized for sensor production, offering a high electrical bias between the substrate and nozzle during printing; this enables in situ dipole alignment, eliminating the need for a separate poling process. The mechanical and electrical stabilities of the sensors are assessed by cyclic testing at 50% strain for 1200 cycles. To illustrate the practical applications of these sensor models, sensors are implemented in wearable and in vivo biomedical applications. [ABSTRACT FROM AUTHOR]

Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition system based on a common wearable device: a smartwatch. This is one of the most inconspicuous devices suitable for activity recognition as it is very common and worn for extensive periods of time. We propose a human activity recognition system that is extensible, due to the wide range of sensing devices that can be integrated, and that provides a flexible deployment system. The machine learning component recognizes activity based on plot images generated from raw sensor data. This service is exposed as a Web API that can be deployed locally or directly in the cloud. The proposed system aims to simplify the human activity recognition process by exposing such capabilities via a web API. This web API can be consumed by small-network-enabled wearable devices, even with basic processing capabilities, by leveraging a simple data contract interface and using raw data. The system replaces extensive pre-processing by leveraging high performance image recognition based on plot images generated from raw sensor data. We have managed to obtain an activity recognition rate of 94.89% and to implement a fully functional real-time human activity recognition system. [ABSTRACT FROM AUTHOR]

With a constantly growing percentage of the population having access to high‐quality healthcare facilities, preventable pathogenic illnesses have been nearly eradicated in the developed parts of the world, which has led to a significant rise in the average human life expectancy over the last few decades. In such a highly developed world, age‐related illnesses will lead to an immense burden on healthcare providers. Remote health monitoring enabled by wearable sensors will play a significant role in the growth and evolution of Health 3.0 by providing intimate and valuable information to healthcare providers regarding the progression of disease in patients with critical life‐altering conditions. Especially, in the case of people suffering from neurodegenerative disorders, inexpensive and user‐friendly wearable sensors can enable physiotherapists monitor real‐time physiological parameters to design patient‐specific treatment plans. This review provides a comprehensive overview of the recent advances and emerging trends at the convergence of biomimicry and nanomaterial sensors, with a specific focus on wearable skin‐inspired mechanical sensors for applications in IoT‐enabled human physiological parameters monitoring. Skin‐inspired wearable mechanical sensors with relevance to the most common types of sensing mechanisms including piezoresistive, piezocapacitive, and triboelectric sensing are discussed along with their current challenges and possible future opportunities. [ABSTRACT FROM AUTHOR]

Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors. [ABSTRACT FROM AUTHOR]