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Machine learning models play a crucial role in sports monitoring by effectively identifying various activities and tracking the number of repetitions during repetitive movements. However, creating models that accurately detect different types of exercises and provide feedback on movement adjustments for wearable devices remains a challenge. In this letter, we propose an alternative approach that addresses this issue by using the restricted Boltzmann machine (RBM) algorithm to learn, evaluate, and provide adjustment feedback based on inertial sensor data in real-time. Our experimental results show that by evaluating body segments individually, highly specialized models can be generated from a small set of movement repetitions. Moreover, these models have the capability to offer users precise recommendations on how to fine-tune the intensity, acceleration, and amplitude of the monitored segment. By using our proposed method, there is a great potential to enhance the accuracy and effectiveness of wearable devices used for sports monitoring. [ABSTRACT FROM AUTHOR]

With the ongoing spread and functional improvement of hearables, we may soon find ourselves in a society where users are wearing hearables at all times. In a hearable environment of this kind, the constant presentation of aural information to users may impede their ability to hear external noises that require their attention. For example, suppose the constant presentation of information in a particular frequency band causes a reduction in the subjective perception of sound pressure (loudness) of the corresponding frequency band. In such a case, the response to environmental sounds that indicate danger (e.g., the sound of an approaching car or an emergency alarm) may be delayed, leading to potential disaster. In this study, we investigated 1) how the presentation of a sound of a specific frequency through a hearable affects the loudness; and 2) which stimulus sound is most effective for recovering the decrease in loudness. In the first investigation, a loudspeaker presented the sound of a specific frequency that imitates environmental sound, and a hearable gave a stimulus sound of a particular frequency based on the frequency of the loudspeaker sound. The results showed that the loudness decreased by more than 10.0% in all stimulus sounds listened to with hearables, and the amount of the decrease tended to be larger the closer the frequency of the loudspeaker sound was to that of the hearable sound. In the second investigation, we hypothesized that the presence of specific recovery stimulus sounds would be effective in quickly restoring any loudness that had decreased, and the results showed that the amount of recovery was greater for all the recovery stimulus sounds we used compared to when the stimulus sounds were not presented.

Falls have a global impact, affecting people worldwide, with a notably high occurrence among the elderly. This study employs machine learning techniques to analyze falls and simulate Activities of Daily Living (ADL). The objective is to predict human falls by leveraging signals from accelerometers and gyroscopes as wearable sensors. By deriving statistical features such as mean, standard deviation, and range the authors successfully trained and assessed six machine learning models allowing them to compare solutions based on both wrist and waist data. The combination of these characteristics and sensors resulted in the Random Forest waist model achieving the most favorable metrics, with an accuracy rate of 97.22% in a 5‐s window. [ABSTRACT FROM AUTHOR]

This research presents the INVys system aiming to solve the problem of indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera. The system utilizes the depth information provided by the camera for micronavigation, which involves sensing and avoiding obstacles in the immediate environment. The INVys system proposes a novel auto-adaptive double thresholding (AADT) method to detect obstacles, calculate their distance, and provide feedback to the user to avoid them. AADT has been evaluated and compared to baseline and auto-adaptive thresholding (AAT) methods using four criteria: accuracy, precision, robustness, and execution time. The results indicate that AADT excels in accuracy, precision, and robustness, making it a suitable method for obstacle detection and avoidance in the context of indoor navigation for persons with visual impairment. In addition to micronavigation, the INVys system utilizes the color information provided by the camera for macro-navigation, which involves recognizing and following navigational markers called optical glyphs. The system uses an automatic glyph binarization method to recognize the glyphs and evaluates them using two criteria: accuracy and execution time. The results indicate that the proposed method is accurate and efficient in recognizing the optical glyphs, making it suitable for use as a navigational marker in indoor environments. Furthermore, the study also provides a correlation between the size of the glyphs, the distance of the recognized glyphs, the tilt condition of the recognized glyphs, and the accuracy of glyph recognition. These correlations define the minimum glyph size that can be practically used for indoor navigation for persons with visual impairment. Overall, this research presents a promising solution for indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera and proposing novel methods for obstacle detection and avoidance and for recognizing navigational markers.

Hearing loss is a common problem affecting the quality of life for thousands of people. However, many individuals with hearing loss are dissatisfied with the quality of modern hearing aids. Amplification is the main method of compensating for hearing loss in modern hearing aids. One common amplification technique is dynamic range compression, which maps audio signals onto a person's hearing range using an amplification curve. However, due to the frequency dependent nature of the human cochlea, compression is often performed independently in different frequency bands. This paper presents a real-time multirate multiband amplification system for hearing aids, which includes a multirate channelizer for separating an audio signal into eleven standard audiometric frequency bands, and an automatic gain control system for accurate control of the steady state and dynamic behavior of audio compression as specified by ANSI standards. The spectral channelizer offers high frequency resolution with low latency of 5.4 ms and about 14× improvement in complexity over a baseline design. Our automatic gain control includes a closed-form solution for satisfying any designated attack and release times for any desired compression parameters. The increased frequency resolution and precise gain adjustment allow our system to more accurately fulfill audiometric hearing aid prescriptions.

Affective computing strives to recognize a person's affective state (e.g., emotion, mood) based on what can be observed. However, electroencephalogram (EEG) and video technologies have not been widely adopted for daily life affect monitoring due to obtrusiveness and privacy concerns. Although the connection between affective states and biophysical data collected with unobtrusive wrist-worn wearables in lab settings has been established successfully, the number of studies for affect recognition in the wild is still limited, and current methods have not yet provided the accuracy necessary for robust applications. In this study, we propose a smart mood-aware emotion detection method. The proposed emotion recognition method extracts the most distinctive features from the physiological data and adds the output of the automated mood detection system as an input to improve performance. The effect of the division of self-report scales into emotion classes is also investigated. The proposed system obtained higher emotion recognition accuracies than most in-the-wild studies when we tested it with the daily life data collected from 14 participants for one week. [ABSTRACT FROM AUTHOR]

Children are increasingly using wearables with physical activity tracking features. Although research has designed and evaluated novel features for supporting parent-child collaboration with these wearables, less is known about how families naturally adopt and use these technologies in their everyday life. We conducted interviews with 17 families who have naturally adopted child-owned wearables to understand how they use wearables individually and collaboratively. Parents are primarily motivated to use child-owned wearables for children's long-term health and wellbeing, whereas children mostly seek out entertainment and feeling accomplished through reaching goals. Children are often unable to interpret or contextualize the measures that wearables record, while parents do not regularly track these measures and focus on deviations from their children's routines. We discuss opportunities for making naturally-occurring family moments educational to positively contribute to children's conceptual understanding of health, such as developing age-appropriate trackable metrics for shared goal-setting and data refection.

Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system's ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Space exploration is evolving with the recent increase in interest and investment. For the success of planned long-duration crewed missions, good interpersonal interactions between crew members are crucial. In this study, we evaluate the use of wearables for detection and estimation of the quality of each social interaction participants have throughout a long mission rather than aggregate measures of interactions. Our proposed method utilizes Temporal Convolutional Networks(TCNs) for extracting individual representations from acceleration and audio streams and learnable pooling layers(NetVLAD) to aggregate these representations into fixed-size representations. Use of NetVLAD layers provides an intelligent alternative to simple aggregation for handling variable-sized interactions and interactions with missing data. We evaluate our method on a 4-month simulated space mission where 5 participants wore Sociometric Badges and provided reports on their interactions in terms of effectiveness, frustration, and satisfaction. Our method provides an average ROC-AUC score of 0.64. Since we are not aware of any comparable baselines, we compare our method to hand-crafted features formerly utilized for cohesion estimation in similar scenarios and show it significantly outperforms them. We also present ablation studies where we replace the components in our approach with well-known alternatives and show that they provide better performance than their respective counterparts. [ABSTRACT FROM AUTHOR]

Interpersonal attraction is known to motivate behavioral responses in the person experiencing this subjective phenomenon. Such responses may involve the imitation of behavior, as in mirroring or mimicry of postures or gestures, which have been found to be associated with the desire to be liked by an interlocutor. Speed dating provides a unique opportunity for the study of such behavioral manifestations of interpersonal attraction through the elimination of barriers to initiating communication, while maintaining significant ecological validity. In this paper we investigate the relationship between body movement, measured via accelerometer sensors, and self-reports or ratings of attraction and affiliation in a dataset of 399 speed dates between 72 subjects. Through machine learning experiments, we found that both features derived from a single individual's body movement and features designed to measure aspects of synchrony and convergence of the couple's body movement signals were predictive of different attraction ratings. Our statistical analysis revealed that the overall increase or decrease in an individual's body movement throughout an interaction is a potential indicator of friendly intentions, possibly related to the desire to affiliate. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile and wearable devices, it is increasingly possible to access users’ affective data in a more unobtrusive manner. On this basis, researchers have proposed various systems to recognize user’s emotional states. However, most of these studies rely on traditional machine learning techniques and a limited number of signals, leading to systems that either do not generalize well or would frequently lack sufficient information for emotion detection in realistic scenarios. In this paper, we propose a novel attention-based LSTM system that uses a combination of sensors from a smartphone (front camera, microphone, touch panel) and a wristband (photoplethysmography, electrodermal activity, and infrared thermopile sensor) to accurately determine user’s emotional states. We evaluated the proposed system by conducting a user study with 45 participants. Using collected behavioral (facial expression, speech, keystroke) and physiological (blood volume, electrodermal activity, skin temperature) affective responses induced by visual stimuli, our system was able to achieve an average accuracy of 89.2 percent for binary positive and negative emotion classification under leave-one-participant-out cross-validation. Furthermore, we investigated the effectiveness of different combinations of data signals to cover different scenarios of signal availability. [ABSTRACT FROM AUTHOR]

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.

Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in wearable human activity recognition (HAR), which has become a new research trend in ubiquitous computing scenario. Increasing network depth or width can further improve accuracy. However, in order to obtain the optimal HAR performance on mobile platform, it has to consider a reasonable tradeoff between recognition accuracy and resource consumption. Improving the performance of CNNs without increasing memory and computational burden is more beneficial for HAR. In this article, we first propose a new CNN that uses hierarchical-split (HS) idea for a large variety of HAR tasks, which is able to enhance multiscale feature representation ability via capturing a wider range of receptive fields of human activities within one feature layer. Experiments conducted on benchmarks demonstrate that the proposed HS module is an impressive alternative to baseline models with similar model complexity, and can achieve higher recognition performance (e.g., 97.28%, 93.75%, 99.02%, and 79.02% classification accuracies) on UCI-HAR, PAMAP2, WISDM, and UNIMIB-SHAR. Extensive ablation studies are performed to evaluate the effect of the variations of receptive fields on classification performance. Finally, we demonstrate that multiscale receptive fields can help to learn more discriminative features (achieving 94.10% SOTA accuracy) in weakly labeled HAR dataset. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) and enterprise management systems (EMS) have been rapidly growing and applied in advanced Industries. It provides better big data analytics and the most promising computing platforms. Moreover, IoT is transforming into the augmented intelligence of things (AIoT), developing a human-oriented paradigm for enterprises with AI. Still, smart enterprises and industries have additional requirements, such as device and data trust, robust decision-making, communication latency, and secure data storage. However, previous emerging paradigms and approaches did not fully address all of the aforementioned requirements. Therefore, this article proposes a blockchain-based trust management scheme for smart enterprises with augmented intelligence. The blockchain-based device trust authentication mechanism is used at the device connection layer for device authentication in clusters of IoT devices (smart enterprises branch-SEB). Furthermore, the blockchain-based augmented intelligence enabled approach is leveraged for data authentication at the authentication layer. Finally, smart enterprise data are stored in the distributed hash table (DHTs) and decentralized cloud layer with distributed hash table. We evaluated the proposed scheme using qualitative and quantitative analysis and compared it to the existing studies, showing better performance as 40.887-ms computational cost and 1872-bits transactional cost. [ABSTRACT FROM AUTHOR]

The rapid developments in Internet of Medical Things open up new avenues for personalized healthcare. Continuously monitored physiological data can be collected by wearable devices and are transmitted to a remote server for real-time monitoring and diagnosis. This article concerns a risk assessment problem of acute mountain sickness (AMS) with data transmitted according to an event-triggered transmission schedule. An event-triggered signal processing approach is introduced to reconstruct the untransmitted information, based on which, a dynamic SpO $_{\bf 2}$ index (DSI) is further proposed for AMS risk evaluation. The performance of the proposed approach is analyzed through physiological data collected in a proof-of-the-concept study (N=12). Statistical significant correlation of the DSI with AMS ground truth including Lake Louise score, deep sleep duration, deep sleep ratio, and mean SpO $_{\bf 2}$ during sleep is observed. More importantly, it is observed that the proposed event-triggered signal processing procedure can dramatically reduce the data transmission rate while maintaining the performance of the DSI assessment, through comparison of the DSI obtained using the proposed event-triggered approach with those obtained based on event-triggered raw data and continuously transmitted time-triggered data. The obtained results indicate the feasibility of adopting event-triggered data scheduling and signal processing to achieve AMS risk evaluation using data from wearable devices with limited communication/battery resources. [ABSTRACT FROM AUTHOR]

Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This article proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMUs) in wearable devices. Our goal is to create a generalized model that can be used for preclinical screening. As a result, rather than classifying only one subtype of DEM, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD), we classify all subtypes as DEM. We also utilize feature selection methods on features from TUG tasks to optimize the MCI and DEM classification performance. From the results, our generalized model can outperform other works in normal control (NC)-MCI&DEM classification with an accuracy of 86.94% and sensitivity of 97.40%. For NC-DEM classification, the performance of our generalized model is slightly lower than that of specific-subtype models (e.g., NC versus AD). However, our generalized model can outperform the specific-subtype models when using a diverse variety of subtypes. It is a reasonable tradeoff, and it can be a good first step toward a future where the patient can preclinically self-screening the cognitive impairments using wearable devices in free-living environments. This could allow patients to notice the cognitive impairment early on and seek a comprehensive diagnosis from a doctor. [ABSTRACT FROM AUTHOR]

Real-time detection of atrial fibrillation (AFib) is made possible by the quality assessment via a 1-D convolutional neural network (1D-CNN) in a processor of a photoplethysmography (PPG) sensor patch and a 2-D convolutional neural network (2D-CNN) for AFib detection in cloud. The 1D-CNN is able to filter out the unqualified PPG that is contaminated by motion artifacts and/or ambient light interference. The remaining qualified PPG is then inputted to another built 2D-CNN for detecting AFib. This 1D-CNN consists of four layers of convolutions and max pooling, one long short-term memory (LSTM), and an output dense layer. The 2D-CNN is pretrained based on the electrocardiography (ECG) data from multiparameter intelligent monitoring in intensive care (MIMIC) III database, for which the RR-intervals (RRIs) of ECG data are first extracted in Poincaré images and then regarded as input features to the model for training. This 2D-CNN has also four layers of convolutions and max pooling and four output dense layers. The pretrained model is next fine-tuned based on peak-to-peak intervals (PPIs) of PPG measured from wearable devices as input features for detecting AFib effectively. The quality-assessment 1D-CNN model is implemented in the wearable device to transmit only qualified data to the 2D-CNN model in cloud for AFib detection, achieving power efficiency. Both models are trained by the Adam optimizer. To validate the models, the PPIs of PPG were collected to evaluate the performance of the established models in real time. Experimental results show that the fine-tuned 2D-CNN for AFib detection achieves the accuracy, sensitivity, and specificity were 98.08%, 96.82%, and 98.86%, respectively, the most favorable as opposed to other reported works based on PPG. The models are able to not only assist clinicians in AFib detection but also provide a mechanism to detect AFib via wearable devices in real time. [ABSTRACT FROM AUTHOR]

The rising aging population, inequality of medical resources, and severe COVID-19 infection rate raise inevitable individual and social contradictions. One of the representative developing technologies, smart wearables, is dedicated to offering accurate personal healthcare. Nevertheless, energy constraints as well as unpredictable data transmission are critical in the development of wearable devices. In this regard, we investigate the key concerns of energy life and quality of service (QoS) for smart clothing. Unlike general wireless sensing networks (WSNs), the wireless body area network (WBAN) embedded in smart clothing is highly affected by human postural changes. In this article, we formulate the smart clothing with multiposture participated from two perspectives: 1) for energy life, we address the energy consumption, the energy harvested by the nodes, and the battery discharge and 2) the QoS involves the path loss and time delay. Moreover, five typical daily activity states have been discussed to model the impact of posture changes. Under the influence of the posture state, the tradeoff between the collected tribological electrical energy and the consumed energy is also presented in the article. We parameterize the path loss, transmission delay, energy consumption, and collection in each posture and integrally formulate the energy problem and QoS to a joint optimization problem. Particle swarm optimization (PSO), sine cosine algorithm (SCA), and Q-learning algorithm are adopted to optimize the overall cost, time delay, and energy consumption. In addition, a comparison of the battery power of the nodes is conducted. Simulation results show that each algorithm achieves certain optimization effects, for example, PSO, SCA, and Q-learning reduce total costs by 14%, 22%, and 30%, respectively. Q-learning is also effectively decreasing latency and energy consumption and improving battery life. [ABSTRACT FROM AUTHOR]

Nowadays, the flexible localization solution for various devices for workplace safety is one of the most demanding research questions. Notably, it is expected to provide an acceptable level of precision in different types of environments empowered by wearable technology and Internet-of-Things (IoT) devices. Existing leading localization technologies are adapted for certain conditions, for example, Wi-Fi, Bluetooth low energy (BLE), and ultra-wideband (UWB) are used for indoor areas and various global navigation satellite system (GNSS)-based ones for outdoors. This work focuses on investigating the long-range wide-area network (LoRaWAN) (868-MHz band) as a potential candidate to bridge this gap, being one of the most reliable and recognized communication technologies for the Industrial IoT (IIoT). In the past, the research community had a lot of critics with respect to the applicability of LoRaWAN for localization, while the vision is facing tremendous change over the past two years. The purpose of this work is to assess the feasibility of LoRaWAN as a localization solution for work safety applications in the industrial scenario from different angles. The work is based on two measurement campaigns conducted at the Brno University of Technology (BUT), Brno, Czech Republic, and the University Politechnica of Bucharest (UPB), Bucharest, Romania. The campaigns cover both indoor and outdoor scenarios and provide the practical limitations of the positioning in standalone and ${k}$ -nearest neighbors (${k}$ -NN) powered localization systems. According to the results, LoRaWAN-based localization with relatively dense gateways (GWs) deployment allows for achieving a meter-level accuracy, which may be suitable for the localization of workers. [ABSTRACT FROM AUTHOR]

This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13 to 22 percent of energy, while achieving similar pattern recognition performance and reconstruction error. [ABSTRACT FROM AUTHOR]

Hearing loss is a common problem affecting the quality of life for thousands of people. However, many individuals with hearing loss are dissatisfied with the quality of modern hearing aids. Amplification is the main method of compensating for hearing loss in modern hearing aids. One common amplification technique is dynamic range compression, which maps audio signals onto a person’s hearing range using an amplification curve. However, due to the frequency dependent nature of the human cochlea, compression is often performed independently in different frequency bands. This paper presents a real-time multirate multiband amplification system for hearing aids, which includes a multirate channelizer for separating an audio signal into eleven standard audiometric frequency bands, and an automatic gain control system for accurate control of the steady state and dynamic behavior of audio compression as specified by ANSI standards. The spectral channelizer offers high frequency resolution with low latency of 5.4 ms and about $14\times $ improvement in complexity over a baseline design. Our automatic gain control includes a closed-form solution for satisfying any designated attack and release times for any desired compression parameters. The increased frequency resolution and precise gain adjustment allow our system to more accurately fulfill audiometric hearing aid prescriptions.

This paper proposes a robust method to screen patients with sleep apnea syndrome (SAS) using a single-lead electrocardiogram (ECG). This method consists of minute-by-minute abnormal breathing detection and apnea-hypopnea index (AHI) estimation. Heartbeat interval and ECG-derived respiration (EDR) are calculated using the single-lead ECG and used to train the models, including ResNet18, ResNet34, and ResNet50. The proposed method, using data from 1232 subjects, was developed with two open datasets and experimental data and evaluated using two additional open datasets and data acquired from an abdomen-attached wearable device (in total, data from 189 subjects). ResNet18 showed the best results, having an average Cohen's kappa coefficient of 0.57, in the abnormal breathing detection. Moreover, SAS patient classification, with 15 as the AHI threshold, yielded an average Cohen's kappa coefficient of 0.71. The results of patient classification were biased toward data from the wearable patch-type device, which may be influenced by different ECG waveforms. The proposed method is tuned with a sample of the data from the device, and the performance result of Cohen's kappa increased from 0.54 to 0.91 for SAS patient classification. Our method, proposed in this paper, achieved equivalent performance results with data recorded using an abdomen-attached wearable device and two open datasets used in previous studies, although the method had not used those data during model training. The proposed method could reduce the development costs of commercial software, as it was developed using open datasets, has robust performance throughout all datasets. [ABSTRACT FROM AUTHOR]

Haptic feedback is known to enhance the realism of an individual’s interactions with objects in virtual environments. Wearable haptic devices, such as vibrotactile sleeves or armbands, can provide haptic feedback in a smaller and more lightweight form factor than haptic gloves that can be bulky and cumbersome to the wearer. In this article, we present tactile and squeeze bracelet interface (Tasbi), a multimodal haptic wristband that can provide radial squeeze forces around the wrist along with vibrotactile feedback at six discrete locations around the band. Tasbi implements a squeezing mechanism that minimizes tangential forces between the band’s points of contact with the skin, instead of focusing the motor actuation to predominantly normal forces. Force sensing capacitors enable closed-loop control of the squeeze force, while vibration is achieved with linear resonant actuators. A detailed description of the design and experimental results demonstrating closed-loop control of squeeze cues provided by Tasbi is presented. Additionally, we present the results of psychophysical experiments that quantify user perception of the vibration and squeeze cues, including vibrotactile identification accuracy in the presence of varying squeeze forces, discrimination thresholds for the squeeze force, and an analysis of user preferences for squeeze actuation magnitudes. [ABSTRACT FROM AUTHOR]

Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multi-branch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from electrocardiogram (ECG) and inertial measurement unit (IMU) data, and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method. [ABSTRACT FROM AUTHOR]

In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing. [ABSTRACT FROM AUTHOR]

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for evaluating the benchmark using a deep long short-term memory (LSTM) neural network, which demonstrated a significant performance improvement over the state-of-the-art human activity recognition (HAR) area. To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3-D point cloud labeling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep-learning methods to reach their full potential in 3-D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company-supported multiple input multiple output (MIMO) radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking. Furthermore, it also investigated sensor locations and requirements for human being data collection that is from a single subject to multiple subjects, as well as identified and analyzed various sensing devices and applications that collect activity data. In this regard, a thorough study is conducted on several benchmark datasets, examining sensors, characteristics, activity categories, and other data. Finally, it compares and analyzes the activity recognition methods used in several benchmark datasets based on the current study. Unlike existing devices, the new NodeNs sensor provides more accessible and straightforward point cloud data to capture human movement information. Depending on an advanced detection algorithm to process point cloud data, it achieved more than 95% accuracy on the benchmark dataset. [ABSTRACT FROM AUTHOR]

Given the widespread adverse outcomes of stress – exacerbated by the current pandemic – wearable sensing provides unique opportunities for automated stress tracking to inform well-being interventions. However, its success in the wild and at scale depends on the robustness and validity of automated stress inference, which is limited in current systems. In this work, we enumerate the properties of robustness and validity necessary for achieving viable automated stress inference using wearable sensors, and we underscore present challenges to constructing and evaluating these systems. Using these criteria as guiding principles, we present automated stress inference results from a large (N=606) in situ longitudinal wearable and contextual sensing study of information workers. Using a multimodal approach encompassing a wearable sensor, relative location tracking, smartphone usage, and environmental sensing, we trained regression models to predict daily self-reported perceived stress in a participant-independent fashion. Our models significantly outperformed baseline variants with shuffled stress scores and were consistent with small-to-moderate effects. Our findings highlight the performance disparity between robust and valid approaches to automated perceived stress inference and current approaches and suggest that further performance gains might require additional sensing modalities and enhanced contextual awareness than existing approaches. [ABSTRACT FROM AUTHOR]

With the technological development in healthcare, environments such as rehabilitation clinics and patients’ houses, are increasingly monitored by multi-device systems. To aggregate information, overcome privacy issues and device failures, it is essential to match measurements from different sources and associate them to a particular patient. While cameras are used to detect and track anonymized persons, wearable devices can acquire inertial and health information. The challenge is to correctly pair the tracked persons to their status, such as heart rate, collected by other devices. Recently, many works have been proposed, in several scenarios, to tackle sensor fusion-based tracking using a large variety of information. However, when the budget is limited, the involved sensing devices lack of inertial components, such as gyroscope, and may have low precision. In this work, we propose a novel solution to match unlabeled 3D skeletons, detected by a depth camera, with on-wrist wearable devices equipped only with accelerometer. Additionally, a Deep Learning submodule, named SkeletonRNN, is introduced to overcome camera failures in the 3D skeletons points detection and fill missing joints. A complete dataset containing skeletons and accelerations measurements, of dailies and rehabilitation activities, has been collected, manually annotated, and is available for testing purposes. We trained and tested the SkeletonRNN using data augmentation on our dataset, the final average 3D point prediction error is 11.00cm and the skeleton-device pairing accuracy of the overall system is 76.62% on a total of 231 chucks. Datasets, code and experiments can be found at https://github.com/matteo-bastico/SkeletonRNN. [ABSTRACT FROM AUTHOR]

Nowadays, use of the dynamic electrocardiogram (ECG) has developed rapidly because of the wide application of wearable devices [1]–[3]. Most ECG-based diagnostic algorithms require that the ECG signal have a clear waveform and accurate feature points. However, the collected wearable ECG signal usually contains a certain amount of noise and causes many false alarms in the ECG analysis system [4], [5]. Thus, signal quality assessment (SQA) plays a prominent role in ruling out the ECG segments with poor signal quality [6]. Compared with traditional static ECG signals, dynamic wearable ECGs contain more noise, which brings greater challenges to disease detection algorithms [7]–[9]. These artifacts and noises in dynamic ECG signals can seriously affect the R-peaks detection, ECG beat extraction, ECG morphological feature extraction and the detection of noise peaks, resulting in frequent false alarms [10]. In 2008, Li et al. [11] proposed the bSQI signal quality indexes: comparison of two beat detectors on a single ECG lead. Liu et al. [12] generalized the two QRS wave complex (QRS) detectors-based bSQI to multiple QRS detectors-based bSQI (GbSQI) to improve the SQA performance. Liu et al. [8] proposed an efficient real-time SQA method for healthy subjects. [ABSTRACT FROM AUTHOR]

Wearable computing has been used in a wide range of applications. But wearable computing often suffers from various security and privacy issues. To solve these issues, many effective authentication schemes have been proposed. However, most of the existing schemes are vulnerable to various known attacks (such as desynchronization attack, privileged-insider attack, and anonymity attack), or require high computation and communication costs, and are not suitable for resource-constrained wearable devices, or simultaneous verification of multiple wearable devices is not supported. Therefore, in this paper, we propose a new anonymous authentication and group proof protocol for wearable computing, which achieves mutual authentication between the wearable device and user and between user and cloud server, and generates a group proof for multiple wearable devices. Further, we extend the Real-Or-Random (ROR) model to support anonymity and group proof, and formally prove that the proposed scheme is provably secure under the extended security model. In addition, the informal security analysis is demonstrated that the proposed scheme is more resilient against known attacks. Finally, compared with some existing schemes, the proposed scheme offers more functionality features and requires less communication and computation costs. [ABSTRACT FROM AUTHOR]

In the last fifteen years, there has been a widespread diffusion of wearable sensorized devices for a plethora of applications in heterogeneous domains. Wearable technology provides fundamental capabilities such as smart sensing, monitoring, data recording, and multi-modal interaction, in a seamless, pervasive, and easy-to-use way. An emerging research trend is the definition of situation-aware wearable computing systems, i.e., wearable devices able to perceive and understand what is happening in the environment in order to adapt their behavior and anticipate users’ needs, a capability known as situation awareness. Although the increasing interest of the research community in situation-aware wearable devices, there is a lack of studies, formal models, methodological approaches, and theoretical groundings on which these systems can be grounded. As a result, a very limited number of smart sensors (physical or virtual) capable of effectively and efficiently supporting Situation Awareness have been proposed so far. In this article, we provide a survey and a classification of state-of-the-art situation-aware wearable systems, outlining current research trends, shortcomings, and challenges, with an emphasis on the models, approaches, and computational techniques of situation awareness and wearable computing on which they are based. The survey has been performed using the PRISMA methodology for systematic reviews. The analysis has been conducted with respect to a reference architecture, namely SA-WCS, of a generic situation-aware wearable computing system that we propose in this article, grounded on Endsley’s model of Situation Awareness. Such reference architecture not only provides a systematic framework for the comparison and categorization of the works, it also aims to promote the development of the next generation WCS. [ABSTRACT FROM AUTHOR]

Advances in embedded systems have given rise to integrating several small-size health monitoring devices within daily human life. This trend led to an ongoing extension of wearable sensors in a broad range of applications. Wearable technologies, which are firmly connected with the human body, utilize sensors and machine learning to describe individuals’ physical or psychological routines through activity recognition and human movement. Since wearables are used all day long, the power consumption of these systems needs to be reasonably low. Current research considers that such machine learning methods are trained with fixed properties, including sensor sampling rate and statistical features computed from the time series data. However, in reality, wearables require continuous reconfiguration of their computational algorithms due to the personalized nature of human gait and movement. Furthermore, computational algorithms must become energy- and memory-efficient due to these embedded sensors’ limited power and memory. In this paper, we propose a resource-efficient framework for real-time, continuous, and on-node human activity recognition. Typically activity recognition problem is a multi-class classification problem. However, we suggest transforming this problem based on MET (Metabolic Equivalent of Task) into a hierarchical classification model, providing personalized structure for each individual. We discuss the design and construction of this new configurable classification paradigm. Our results demonstrate that the proposed probabilistic cascading system accuracy for different personalized scenarios varies between 94.5% and 96.9% in detecting activities using a limited memory, while power usage of the system is reduced by as high as 17.2% compared to the traditional methods. [ABSTRACT FROM AUTHOR]

Assessment of physical performance is essential to predict the frailty level of older adults. The modified Physical Performance Test (mPPT) clinically assesses the performance of nine activities: standing balance, chair rising up & down, lifting a book, putting on and taking off a jacket, picking up a coin, turning 360°, walking, going upstairs, and going downstairs. The activity performing duration is the primary evaluation standard. In this study, wearable devices are leveraged to recognize and predict mPPT items’ duration automatically. This potentially allows frequent follow up of physical performance, and facilitates more appropriate interventions. Five devices, including accelerometers and gyroscopes, were attached to the waist, wrists and ankles of eight younger adults. The system was experimented within three aspects: machine learning models, sensor placement, and sampling frequencies, to which the non-causal six-stages temporal convolutional network using 6.25 Hz signals from the left wrist and right ankle obtained the optimal performance. The duration prediction error ranged from 0.63±0.29 s (turning 360°) to 8.21±16.41 s (walking). The results suggest the potential for the proposed system in the automatic recognition and segmentation of mPPT items. Future work includes improving the recognition performance of lifting a book and implementing the frailty score prediction. [ABSTRACT FROM AUTHOR]

Vibratory stimulation may improve post-stroke symptoms such as spasticity; however, current studies are limited by the large, clinic-based apparatus used to apply this stimulation. A wearable device could provide vibratory stimulation in a mobile form, enabling further study of this technique. An initial device, the vibrotactile stimulation (VTS) Glove, was deployed in an eight-week clinical study in which sixteen individuals with stroke used the device for several hours daily. Participants reported wearing the glove during activities such as church, social events, and dining out. However, 69% of participants struggled to extend or insert their fingers to don the device. In a follow-up study, eight individuals with stroke evaluated new VTS device prototypes in a three-round iterative design study with the aims of creating the next generation of VTS devices and understanding features that influence interaction with a wearable device by individuals with impaired upper-limb function. Interviews and interaction tasks were used to define actionable design revisions between each round of evaluation. Our analysis identified six new themes from participants regarding device designs: hand supination is challenging, separate finger attachments inhibit fit and use, fingers may be flexed or open, fabric coverage impacts comfort, a reduced concern for social comfort, and the affected hand is infrequently used. Straps that wrap around the arm and fixtures on the anterior arm were other challenging features. We discuss potential accommodations for these challenges, as well as social comfort. New VTS device designs are presented and were donned in an average time of 48 seconds. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI), a communication technology between brain and computer developed for a long time since the 1970s, can be incorporated into wearable devices by developing powerful signal processing algorithms and semiconductor technologies. For a satisfactory user experience based on BCI, high information transfer rate and low power consumption should be considered together without losing accuracy. Although many existing BCI algorithms have been mainly focused solely on the accuracy, their deployment on wearable devices is not straightforward due to the limited hardware resources and computational capabilities. This tutorial summarizes recent advances in wearable BCI algorithms and hardware implementations from an algorithm-hardware co-design perspective and discusses future directions. [ABSTRACT FROM AUTHOR]

In the foreseeable future, mobile and wearable computing technology with an augmented reality (AR) interface can provide an omnipresent, environmentally adaptive, and everyday reality augmentation. This new pervasive AR technology will lead to a continuous moderation of experienced reality with the potential to support better and faster decision-making, the exploration of new information, and novel ways of communication, interaction, and collaboration. However, pervasive AR technology will also have undesired consequences, e.g., in the areas of privacy, commercial exploitation, distractions, digital inequality, and our perception of what is true and real. Little is known about how severe these effects will be when AR has become pervasive and how they can be prevented or mitigated. We draw on current developments in research and the market, sketch a near-time future of pervasive AR technology, identify ethical considerations, and discuss the development of pervasive AR systems. [ABSTRACT FROM AUTHOR]

This article reflects on the visions and motivations underlying Pervasive Computing and advances made ending with considering future directions for the field. It describes these in terms of four phases: 1) vision-inspired, 2) the design of engaging experiences, 3) innovation-based, and 4) addressing societal challenges. It is proposed that in the future we will need to embrace a paradigm shift that will be far more challenging than previously. While we can continue to harness pervasive computing advances to augment ever more aspects of ourselves and the environment, we will need in the current climate to be more mindful and responsible of our aspirations. This may mean, paradoxically, contemplating how the field scales down its technology innovation in order to scale up its impact. This article sets out how to achieve this. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) use geographic information to provide a variety of benefits to users—most ubiquitously, through users’ mobile devices (phones and smart wearables). While such information offers value—through navigation apps, for example—it contains troves of personal data that can be exploited against users. [ABSTRACT FROM AUTHOR]

Objective: In order to evaluate Parkinson disease patients’ response to therapeutic interventions, sources of information are mainly patient reports and clinicians’ assessment of motor functions. However, these sources can suffer from patient's subjectivity and from inter/intra rater's score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. Methods: Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. Results: According to our findings, levodopa-based therapy alters the patient's conditions in general, ameliorating something (e.g., bradykinesia), leaving unchanged others (e.g., tremor), but with poor correlation to the levodopa dose. Conclusion: A technology-based approach can objectively assess levodopa-based therapy effectiveness. Significance: Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports. [ABSTRACT FROM AUTHOR]