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Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932   ±   0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function. [ABSTRACT FROM AUTHOR]

Stroke is a medical condition characterized by the rapid loss of focal brain function. Post-stroke patients attend rehabilitation training to prevent the degeneration of physical function and improve upper limb movements and functional status after stroke. Promising rehabilitation therapies include functional electrical stimulation (FES), exergaming, and virtual reality (VR). This work presents a biomechanical assessment of 13 post-stroke patients with hemiparesis before and after rehabilitation therapy for two months with these three methods. Patients performed two tests (Maximum Forward Reach and Apley Scratching) where maximum angles, range of motion, angular velocities, and execution times were measured. A Wilcoxon test was performed (p = 0.05) to compare the variables before and after the therapy for paretic and non-paretic limbs. Significant differences were found in range of motion in flexion–extension, adduction–abduction, and internal–external rotation of the shoulder. Increases were found in flexion–extension, 17.98%, and internal–external rotation, 18.12%, after therapy in the Maximum Forward Reach Test. For shoulder adduction–abduction, the increase found was 20.23% in the Apley Scratching Test, supporting the benefits of rehabilitation therapy that combines FES, exergaming, and VR in the literature. [ABSTRACT FROM AUTHOR]

Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports. Results: CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise. Conclusions: CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users. [ABSTRACT FROM AUTHOR]

The 6-min walk test (6MWT) is commonly used to assess a person's physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees. [ABSTRACT FROM AUTHOR]

The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under unsupervised daily life conditions. Since thigh orientation from horizontal to vertical is characteristic for STS movement, peak angular velocity (PAV) of the thigh was chosen as the outcome variable. A total of 20 younger and older healthy persons and geriatric patients (mean age: 55.5 ± 20.8 years; 55% women) with a wide range of STS performance were instructed to stand up from a chair at their usual pace. STS performance was measured by an activity monitor, force plates, and an opto-electronic system. The association between PAV measured by the thigh-worn activity monitor and PAV measured by the opto-electronic system (gold standard) was r = 0.74. The association between PAV measured by the thigh-worn activity monitor and peak power measured by force plate and opto-electronic system was r = 0.76. The Intra-Class Coefficient (ICC) of agreement between the 2 trials was ICC(A,1) = 0.76. In this sample of persons with a wide range of physical performance, PAV as measured by a thigh-worn acceleration sensor was a valid and reliable measure of STS performance. [ABSTRACT FROM AUTHOR]

Recent tragedies around the world have shown how accidents in the cable-stayed bridges can wreak havoc on the society. To ensure the safety of the cable-stayed bridges, several studies have estimated the cable tension force using the vibration of cables. Most of these methods for estimating the tension of a cable start with measuring the displacement of the cable. Recent development of commercial cameras provide opportunity for more convenient and efficient method for measuring the displacement of cable. However, traditional vision-based displacement measurement methods require the assumption that the movement of the cable should be measured in parallel to the camera plane. This assumption limits the installation location of the camera when measuring the displacement of a cable. Therefore, this study introduces a new vision-based cable displacement measurement system that can measure the displacement of a cable in various locations even when the camera is installed in the side of the cable. The proposed method consists of three phases: (1) camera projection matrix estimation, (2) cable tracking in the image coordinate, and (3) cable displacement estimation in the world coordinate. To validate the performance of the proposed method, a simulation-based validation test, a lab-scale validation test, and an on-site validation test were conducted. The simulation-based validation test verified the performance of the proposed method in an ideal condition, and the lab-scale validation test showed the performance of the method in physical environment. Finally, the on-site validation test showed that the proposed method can measure the cable displacement with a side view camera. [ABSTRACT FROM AUTHOR]

The Timed Up and Go (TUG) test quantifies physical mobility by measuring the total performance time. In this study, we quantified the single TUG subcomponents and, for the first time, explored the effects of gait cycle and pelvis asymmetries on them. Transfemoral (TF) and transtibial (TT) amputees were compared with a control group. A single wearable inertial sensor, applied to the back, captured kinematic data from the body and pelvis during the 10-m walk test and the TUG test. From these data, two categories of symmetry indexes (SI) were computed: One SI captured the differences between the antero-posterior accelerations of the two sides during the gait cycle, while another set of SI quantified the symmetry over the three-dimensional pelvis motions. Moreover, the total time of the TUG test, the time of each subcomponent, and the velocity of the turning subcomponents were measured. Only the TF amputees showed significant reductions in each SI category when compared to the controls. During the TUG test, the TF group showed a longer duration and velocity reduction mainly over the turning subtasks. However, for all the amputees there were significant correlations between the level of asymmetries and the velocity during the turning tasks. Overall, gait cycle and pelvis asymmetries had a specific detrimental effect on the turning performance instead of on linear walking. [ABSTRACT FROM AUTHOR]

The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers. [ABSTRACT FROM AUTHOR]

Low-power and secure crypto-devices are in crucial demand for the current emerging technology of the Internet of Things (IoT). In nanometer CMOS technology, the static and dynamic power consumptions are in a very critical challenge. Therefore, the FinFETs is an alternative technology due to its superior attributes of non-leakage power, intra-die variability, low-voltage operation, and lower retention voltage of SRAMs. In this study, our previous work on CMOS two-phase clocking adiabatic physical unclonable function (TPCA-PUF) is evaluated in a FinFET device with a 4-bits PUF circuit complexity. The TPCA-PUF-based shorted-gate (SG) and independent-gate (IG) modes of FinFETs are investigated under various ambient temperatures, process variations, and ±20% of supply voltage variations. To validate the proposed TPCA-PUF circuit, the QUALPFU-based Fin-FETs are compared in terms of cyclical energy dissipation, the security metrics of the uniqueness, the reliability, and the bit-error-rate (BER). The proposed TPCA-PUF is simulated using 45 nm process technology with a supply voltage of 1 V. The uniqueness, reliability, and the BER of the proposed TPCA-PUF are 50.13%, 99.57%, and 0.43%, respectively. In addition, it requires a start-up power of 18.32 nW and consumes energy of 2.3 fJ/bit/cycle at the reference temperature of 27 °C. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual's mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson's disease, Alzheimer's disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson's cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials. [ABSTRACT FROM AUTHOR]

An athlete's sporting performance depends to a large extent on the technical execution of the athletic motion in order to achieve maximum effectiveness in physical performance. Performance analysis provides an important means of classifying and quantifying athletic prowess in terms of the significant performance aspects of the sport to provide objective feedback. This study aimed to analyze technical execution in terms of punch trajectory, force, velocity and time, considering the expert-novice paradigm by investigating the technical execution of 31 experienced and non-experienced athletes for the four main punching techniques of the cross, jab, uppercut and hook strike. The kinetic and kinematic data were collected by means of a boxing monitoring system developed and validated for in-field use. The research revealed significant correlation for executed punching trajectory and punch force in intragroup comparison and significant differences in intergroup comparison. No significant differences were detected for punch velocity in either inter- or intra-group paradigms. This study, through use of the sensor system, aligns with the results of existing publications conducted in laboratory conditions, in the assessment of punch force, punch speed and punch time and thus extends the state of research by use of a smart wearable in field method. [ABSTRACT FROM AUTHOR]

In this invited review, we provide an overview of the recent advances in biomedical photonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes. [ABSTRACT FROM AUTHOR]

Wireless medical sensor networks (WMSNs) are used in remote medical service environments to provide patients with convenient healthcare services. In a WMSN environment, patients wear a device that collects their health information and transmits the information via a gateway. Then, doctors make a diagnosis regarding the patient, utilizing the health information. However, this information can be vulnerable to various security attacks because the information is exchanged via an insecure channel. Therefore, a secure authentication scheme is necessary for WMSNs. In 2021, Masud et al. proposed a lightweight and anonymity-preserving user authentication scheme for healthcare environments. We discover that Masud et al.'s scheme is insecure against offline password guessing, user impersonation, and privileged insider attacks. Furthermore, we find that Masud et al.'s scheme cannot ensure user anonymity. To address the security vulnerabilities of Masud et al.'s scheme, we propose a three-factor-based mutual authentication scheme with a physical unclonable function (PUF). The proposed scheme is secure against various security attacks and provides anonymity, perfect forward secrecy, and mutual authentication utilizing biometrics and PUF. To prove the security features of our scheme, we analyze the scheme using informal analysis, Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Furthermore, we estimate our scheme's security features, computation costs, communication costs, and energy consumption compared with the other related schemes. Consequently, we demonstrate that our scheme is suitable for WMSNs. [ABSTRACT FROM AUTHOR]

The performance of military tasks is often exacerbated by additional load carriage, leading to increased physical demand. Previous studies showed that load carriage may lead to increased risk of developing musculoskeletal injuries, a reduction in task speed and mobility, and overall performance degradation. However, these studies were limited to a non-ambulatory setting, and the underlying causes of performance degradation remain unclear. To obtain insights into the underlying mechanisms of reduced physical performance during load-carrying military activities, this study proposes a combination of IMUs and musculoskeletal modeling. Motion data of military subjects was captured using an Xsens suit during the performance of an agility run under three different load-carrying conditions (no load, 16 kg, and 31 kg). The physical performance of one subject was assessed by means of inertial motion-capture driven musculoskeletal analysis. Our results showed that increased load carriage led to an increase in metabolic power and energy, changes in muscle parameters, a significant increase in completion time and heart rate, and changes in kinematic parameters. Despite the exploratory nature of this study, the proposed approach seems promising to obtain insight into the underlying mechanisms that result in performance degradation during load-carrying military activities. [ABSTRACT FROM AUTHOR]

The Short Physical Performance Battery (SPPB) is a widely accepted test for measuring lower extremity function in older adults. However, there are concerns regarding the examination time required to conduct a complete SPPB consisting of three components (walking speed, chair rise, and standing balance tests) in clinical settings. We aimed to assess specific examination times for each component of the electronic Short Physical Performance Battery (eSPPB) and compare the ability of the original three-component examinations (eSPPB) and a faster, two-component examination without a balance test (electronic Quick Physical Performance Battery, eQPPB) to classify sarcopenia. The study was a retrospective, cross-sectional study which included 124 ambulatory outpatients who underwent physical performance examination at a geriatric clinic of a tertiary, academic hospital in Seoul, Korea, between December 2020 and March 2021. For eSPPB, we used a toolkit containing sensors and software (Dyphi, Daejeon, Korea) developed to measure standing balance, walking speed, and chair rise test results. Component-specific time stamps were used to log the raw data. Duration of balance examination, 5 times sit-to-stand test (5XSST), and walking speed examination were calculated. Sarcopenia was determined using the 2019 Asian Working Group for Sarcopenia (AWGS) guideline. The median age was 78 years (interquartile range, IQR: 73,82) and 77 subjects (62.1%) were female. The total mean eSPPB test time was 124.8 ± 29.0 s (balance test time 61.8 ± 12.3 s, 49.5%; gait speed test time 34.3 ± 11.9 s, 27.5%; and 5XSST time 28.7 ± 19.1 s, 23.0%). The total mean eQPPB test time was 63.0 ± 25.4 s. Based on the AWGS criteria, 34 (27.4%) patient's results were consistent with sarcopenia. C-statistics for classifying sarcopenia were 0.83 for eSPPB and 0.85 for eQPPB (p = 0.264), while eQPPB took 49.5% less measurement time compared with eSPPB. Breakdowns of eSPPB test times were identified. Omitting balance tests may reduce test time without significantly affecting the classifying ability of eSPPB for sarcopenia. [ABSTRACT FROM AUTHOR]

Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk. Assessment of balance and falls risk using a validated smartphone app may lessen the need for clinical assessments which can be expensive, requiring non-portable equipment and specialist expertise. This study reports results for the real-world deployment of a smartphone app for self-directed, unsupervised assessment of balance and falls risk. The app relies on a previously validated algorithm for assessment of balance and falls risk; the outcome measures employed were trained prior to deployment on an independent data set. Results for a sample of 594 smartphone assessments from 147 unique phones show a strong association between self-reported falls history and the falls risk and balance impairment scores produced by the app, suggesting they may be clinically useful outcome measures. In addition, analysis of the quantitative balance features produced seems to suggest that unsupervised, self-directed assessment of balance in the home is feasible. [ABSTRACT FROM AUTHOR]

This article introduces a new class of physical unclonable functions (PUFs) based on the Fibonacci ring oscillator (FIRO). The research conducted here proves that before reaching the desired randomness, the oscillator shows a certain degree of repeatability and uniqueness in the initial sequence of internal state transitions. The use of an FIRO in conjunction with the restart method makes it possible to obtain a set of short boot sequences, which are processed with an innovative feature extraction algorithm that enables reliable device identification. This approach ensures the reuse of the existing random number generator (RNG), rather than multiplying ring oscillators in a dedicated structure. Moreover, the algorithm for the recovery of the device key from the boot set can be successfully implemented in the authorizing center, thus significantly releasing the resources of authorized low-complexity devices. The proposed methodology provides an easily obtainable key with identifiability, which was proven experimentally on FPGAs from different manufacturers. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) are widely used in blockchain to represent unique and non-interchangeable assets. Current NFTs allow representing assets by a unique identifier, as a possession of an owner. The novelty introduced in this paper is the proposal of smart NFTs to represent IoT devices, which are physical smart assets. Hence, they are also identified as the utility of a user, they have a blockchain account (BCA) address to participate actively in the blockchain transactions, they can establish secure communication channels with owners and users, and they operate dynamically with several modes associated with their token states. A smart NFT is physically bound to its IoT device thanks to the use of a physical unclonable function (PUF) that allows recovering its private key and, then, its BCA address. The link between tokens and devices is difficult to break and can be traced during their lifetime, because devices execute a secure boot and carry out mutual authentication processes with new owners and users that could add new software. Hence, devices prove their trusted hardware and software. A whole demonstration of the proposal developed with ESP32-based IoT devices and Ethereum blockchain is presented, using the SRAM of the ESP32 microcontroller as the PUF. [ABSTRACT FROM AUTHOR]

Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users' acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors. [ABSTRACT FROM AUTHOR]

The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg–Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities. [ABSTRACT FROM AUTHOR]

Background: The recovery of upper limb mobility and functions is essential for people with cervical spinal cord injuries (cSCI) to maximize independence in daily activities and ensure a successful return to normality. The rehabilitative path should include a thorough neuromotor evaluation and personalized treatments aimed at recovering motor functions. Body-machine interfaces (BoMI) have been proven to be capable of harnessing residual joint motions to control objects like computer cursors and virtual or physical wheelchairs and to promote motor recovery. However, their therapeutic application has still been limited to shoulder movements. Here, we expanded the use of BoMI to promote the whole arm's mobility, with a special focus on elbow movements. We also developed an instrumented evaluation test and a set of kinematic indicators for assessing residual abilities and recovery. Methods: Five inpatient cSCI subjects (four acute, one chronic) participated in a BoMI treatment complementary to their standard rehabilitative routine. The subjects wore a BoMI with sensors placed on both proximal and distal arm districts and practiced for 5 weeks. The BoMI was programmed to promote symmetry between right and left arms use and the forearms' mobility while playing games. To evaluate the effectiveness of the treatment, the subjects' kinematics were recorded while performing an evaluation test that involved functional bilateral arms movements, before, at the end, and three months after training. Results: At the end of the training, all subjects learned to efficiently use the interface despite being compelled by it to engage their most impaired movements. The subjects completed the training with bilateral symmetry in body recruitment, already present at the end of the familiarization, and they increased the forearm activity. The instrumental evaluation confirmed this. The elbow motion's angular amplitude improved for all subjects, and other kinematic parameters showed a trend towards the normality range. Conclusion: The outcomes are preliminary evidence supporting the efficacy of the proposed BoMI as a rehabilitation tool to be considered for clinical practice. It also suggests an instrumental evaluation protocol and a set of indicators to assess and evaluate motor impairment and recovery in cSCI. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the influence of wearing a ballistic vest on physical performance in police officers. Methods: We performed a cross-over study to investigate the influence of wearing a ballistic vest on reaction and response time, lumbar muscle endurance and police vehicle entry and exit times. Reaction and response time was based on a perturbation setup where the officers' pelvises were fixed and EMG of lumbar and abdominal muscles was recorded. We used a modified Biering–Sørensen test to assess the lumbar muscle endurance and measured duration of entry and exit maneuvers in a variety of standard-issue police cars. Results: There was a significant difference of 24% in the lumbar muscle endurance test (no vest: 151 s vs. vest: 117 s), and the police officers experienced higher physical fatigue after the test when wearing a vest. Furthermore, officers took longer to both enter and exit police cars when wearing a vest (range: 0.24–0.56 s) depending on the model of the vehicle. There were no significant differences in reaction and response times between the test conditions (with/without vest). Discussion and Conclusion: Wearing of a ballistic vest significantly influenced the speed of movement in entry and exit of police cars and lumbar muscle endurance, although it does not seem to affect reaction or response times. The ballistic vest seems to impair performance of tasks that require maximal effort, which calls for better designs of such vests. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyse the physical demands of U10 players in a 7-a-side-soccer tournament based on the playing positions in 6 consecutive matches by global positioning systems (GPS). Variables of total distance, relative distance in different speed zones, maximum speed, time interval between accelerations, maximum speed acceleration, maximum acceleration, acceleration distance and the number of high-intensity accelerations were analysed. Differences between playing positions were found in the total distance covered by the midfielders. They covered higher total distances than the defenders (+1167 m; 95% CI: 411 to 1922 m; effect size (ES) = 1.41; p < 0.05) and forwards (+1388 m; CI 95%: 712 a 2063 m; TE = 0.85; p < 0.05). The total covered distance increased in the final rounds with respect to the group stage (p < 0.05; ES: 0.44 to 1.62), and high-intensity actions, such as the number of accelerations, were greater in the final rounds compared to the group stage (p < 0.05; ES: 0.44 to 1.62). The physical performance of young football players in a tournament with consecutive matches on a 40 × 62 m football field on the same day is influenced by the playing position and dependent on the level difference between opponents. [ABSTRACT FROM AUTHOR]

The aim of this study was twofold: (i) to analyze the physical demands in the return to play (RTP) process of high-level soccer players in the role of floater in different soccer sided games (SGs) formats (i.e., 4vs4 + 2 and 8vs8 + 1); and (ii) to analyze the differences in physical demands encountered by regular and floater players among the SGs formats and official matches by means of global positioning system technology (GPS APEX pod, North Ireland) was used. Twenty-six highly trained, male soccer players (U16 years) participated in this investigation. Players were classified into two groups: 23 regular and 3 floater players, a total of eight SGs were analyzed, which involved the recording of 80 observations of regular and floater players. Match-play players showed most likely–probable differences in distance covered at high-intensity per minute (D > 14.4/min), at high-speed running per minute (D > 21/min), and peak velocity (Vpeak) in comparison to floaters in the 8vs8 + 1 LSG (large-side-games), and presented most likely differences in accelerations >2/min in comparison to match-play players. Therefore, the use of floaters during the last phase of the RTP (return to play) seems to be a useful strategy for progressive reintroduction into specific training (1) floater in the 4vs4 + 2 SSG; (2) floater in the 8vs8 + 1 LSG; (3) regular player in the 4vs4 + 2 SSG; and (4) regular player in the 8vs8 + 1 LSG before starting full trainings and returning to competition. [ABSTRACT FROM AUTHOR]

Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors' data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security. [ABSTRACT FROM AUTHOR]

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system's accuracy and investigate a wireless alternative based on neural networks. [ABSTRACT FROM AUTHOR]