The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects

Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS.

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.

Performance Evaluation of an Immersive Virtual Reality Application for Rehabilitation after Arthroscopic Rotator Cuff Repair

Few studies have evaluated the effectiveness of shoulder rehabilitation in virtual environments. The objective of this study was to investigate the performance of a custom virtual reality application (VR app) with a stereophotogrammetric system considered the gold standard. A custom VR app was designed considering the recommended rehabilitation exercises following arthroscopic rotator cuff repair. Following the setting of the play space, the user's arm length, and height, five healthy volunteers performed four levels of rehabilitative exercises. Results for the first and second rounds of flexion and abduction displayed low total mean absolute error values and low numbers of unmet conditions. In internal and external rotation, the number of times conditions were not met was slightly higher; this was attributed to a lack of isolated shoulder movement. Data is promising, and volunteers were able to reach goal conditions more often than not. Despite positive results, more literature comparing VR applications with gold-standard clinical parameters is necessary. Nevertheless, results contribute to a body of literature that continues to encourage the application of VR to shoulder rehabilitation programs.

Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

Health Monitoring via Heart, Breath, and Korotkoff Sounds by Wearable Piezoelectret Patches

Real-time monitoring of vital sounds from cardiovascular and respiratory systems via wearable devices together with modern data analysis schemes have the potential to reveal a variety of health conditions. Here, a flexible piezoelectret sensing system is developed to examine audio physiological signals in an unobtrusive manner, including heart, Korotkoff, and breath sounds. A customized electromagnetic shielding structure is designed for precision and high-fidelity measurements and several unique physiological sound patterns related to clinical applications are collected and analyzed. At the left chest location for the heart sounds, the S1 and S2 segments related to cardiac systole and diastole conditions, respectively, are successfully extracted and analyzed with good consistency from those of a commercial medical device. At the upper arm location, recorded Korotkoff sounds are used to characterize the systolic and diastolic blood pressure without a doctor or prior calibration. An Omron blood pressure monitor is used to validate these results. The breath sound detections from the lung/ trachea region are achieved a signal-to-noise ration comparable to those of a medical recorder, BIOPAC, with pattern classification capabilities for the diagnosis of viable respiratory diseases. Finally, a 6×6 sensor array is used to record heart sounds at different locations of the chest area simultaneously, including the Aortic, Pulmonic, Erb's point, Tricuspid, and Mitral regions in the form of mixed data resulting from the physiological activities of four heart valves. These signals are then separated by the independent component analysis algorithm and individual heart sound components from specific heart valves can reveal their instantaneous behaviors for the accurate diagnosis of heart diseases. The combination of these demonstrations illustrate a new class of wearable healthcare detection system for potentially advanced diagnostic schemes.

Monitoring Scapular Kinematics through Wearable Magneto-Inertial Measurement Units: State of the Art and New Frontiers

Monitoring shoulder kinematics, including the scapular segment, is of great relevance in the orthopaedic field. Among wearable systems, magneto-inertial measurement units (M-IMUs) represent a valid alternative for applications in unstructured environments. The aim of this systematic literature review is to report and describe the existing methods to estimate 3D scapular movements through wearable systems integrating M-IMUs. A comprehensive search of PubMed, IEEE Xplore, and Web of Science was performed, and results were included up to May 2023. A total of 14 articles was included. The results showed high heterogeneity among studies regarding calibration procedures, tasks executed, and the population. Two different techniques were described, i.e., with the x-axis aligned with the cranial edge of the scapular spine or positioned on the flat surface of the acromion with the x-axis perpendicular to the scapular spine. Sensor placement affected the scapular motion and, also, the kinematic output. Further studies should be conducted to establish a universal protocol that reduces the variability among studies. Establishing a protocol that can be carried out without difficulty or pain by patients with shoulder musculoskeletal disorders could be of great clinical relevance for patients and clinicians to monitor 3D scapular kinematics in unstructured settings or during common clinical practice.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Smart Wearable Systems for Health Monitoring

Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration, and the number of high-performance wearable systems has been gradually increasing in recent years. However, there are still many challenges in these fields, such as balancing the trade-off between flexibility/stretchability, sensing performance, and the robustness of systems. For this reason, more evolution is required to promote the development of wearable health-monitoring systems. In this regard, this review summarizes some representative achievements and recent progress of wearable systems for health monitoring. Meanwhile, a strategy overview is presented about selecting materials, integrating systems, and monitoring biosignals. The next generation of wearable systems for accurate, portable, continuous, and long-term health monitoring will offer more opportunities for disease diagnosis and treatment.

Robust and Multifunctional Kirigami Electronics with a Tough and Permeable Aramid Nanofiber Framework

Kirigami designs are advantageous for the construction of wearable electronics due to their high stretchability and conformability on the 3D dynamic surfaces of the skin. However, suitable materials technologies that enable robust kirigami devices with desired functionality for skin-interfaces remain limited. Here, a versatile materials platform based on a composite nanofiber framework (CNFF) is exploited for the engineering of wearable kirigami electronics. The self-assembled fibrillar network involving aramid nanofibers and poly(vinyl alcohol) combines high toughness, permeability, and manufacturability, which are desirable for the fabrication of hybrid devices. Multiscale simulations are conducted to explain the high fracture resistance of the CNFF-based kirigami structures and provide essential guidance for the design, which can be further generalized to other kirigami devices. Various microelectronic sensors and electroactive polymers are integrated onto a CNFF-based materials platform to achieve electrocardiogram (ECG), electromyogram (EMG), skin-temperature measurements, and measurement of other physiological parameters. These mechanically robust, multifunctional, lightweight, and biocompatible kirigami devices can shed new insights for the development of advanced wearable systems and human-machine interfaces.

Wearable Systems for Home Monitoring Healthcare: The Photoplethysmography Success Pros and Cons

The widespread use of remote technology has moved medical care services into individuals' homes. In this perspective, the ubiquitous computing research proposes self-management and remote monitoring to help patients with healthcare in low-cost everyday home usage systems based on the latest technological advances in sensors, communication, and portability. This work analyzes recent publications on the paradigm of continuous monitoring through wearable and portable systems, focusing on photoplethysmography (PPG) advances and referencing the current systematic study proposed by Fine et al. The study revised the literature highlighting the pros and cons of using the PPG system for fitness, wellbeing, and medical devices. However, future works should focus on the standardization of the practical use and assessment of the quality of the PPGs' output. For clinical parameter extraction methodology in terms of biological sites of application and signal processing methods, PPG is the most convenient and widely used system potentially suitable for the decentralized paradigm of continuous monitoring healthcare concepts.

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring

The demand for wearable devices to simultaneously monitor heart rate (HR) and respiratory rate (RR) values has grown due to the incidence increase in cardiovascular and respiratory diseases. The use of inertial measurement unit (IMU) sensors, embedding both accelerometers and gyroscopes, may ensure a non-intrusive and low-cost monitoring. While both accelerometers and gyroscopes have been assessed independently for both HR and RR monitoring, there lacks a comprehensive comparison between them when used simultaneously. In this study, we used both accelerometers and gyroscopes embedded in a single IMU sensor for the simultaneous monitoring of HR and RR. The following main findings emerged: (i) the accelerometer outperformed the gyroscope in terms of accuracy in both HR and RR estimation; (ii) the window length used to estimate HR and RR values influences the accuracy; and (iii) increasing the length over 25 s does not provide a relevant improvement, but accuracy improves when the subject is seated or lying down, and deteriorates in the standing posture. Our study provides a comprehensive comparison between two promising systems, highlighting their potentiality for real-time cardiorespiratory monitoring. Furthermore, we give new insights into the influence of window length and posture on the systems' performance, which can be useful to spread this approach in clinical settings.

Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling

Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler's kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized.

Toward a Servoregulation Controller to Automate CO2 Removal in Wearable Artificial Lungs

A laptop-driven, benchtop control system that automatically adjusts carbon dioxide (CO2) removal in artificial lungs (ALs) is described. The proportional-integral-derivative (PID) feedback controller modulates pump-driven air sweep gas flow through an AL to achieve a desired exhaust gas CO2 partial pressure (EGCO2). When EGCO2 increases, the servoregulator automatically and rapidly increases sweep flow to remove more CO2. If EGCO2 decreases, the sweep flow decreases to reduce CO2 removal. System operation was tested for 6 hours in vitro using bovine blood and in vivo in three proof-of-concept sheep experiments. In all studies, the controller automatically adjusted the sweep gas flow to rapidly (<1 minute) meet the specified EGCO2 level when challenged with changes in inlet blood or target EGCO2 levels. CO2 removal increased or decreased as a function of arterial pCO2 (PaCO2). Such a system may serve as a controller in wearable AL systems that allow for large changes in patient activity or disease status.

Towards a Contactless Stress Classification Using Thermal Imaging

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.

Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.

Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units

Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation.

The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis

The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system's estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.

Skin Strain Analysis of the Scapular Region and Wearables Design

Monitoring scapular movements is of relevance in the contexts of rehabilitation and clinical research. Among many technologies, wearable systems instrumented by strain sensors are emerging in these applications. An open challenge for the design of these systems is the optimal positioning of the sensing elements, since their response is related to the strain of the underlying substrates. This study aimed to provide a method to analyze the human skin strain of the scapular region. Experiments were conducted on five healthy volunteers to assess the skin strain during upper limb movements in the frontal, sagittal, and scapular planes at different degrees of elevation. A 6 × 5 grid of passive markers was placed posteriorly to cover the entire anatomic region of interest. Results showed that the maximum strain values, in percentage, were 28.26%, and 52.95%, 60.12% and 60.87%, 40.89%, and 48.20%, for elevation up to 90° and maximum elevation in the frontal, sagittal, and scapular planes, respectively. In all cases, the maximum extension is referred to the pair of markers placed horizontally near the axillary fold. Accordingly, this study suggests interesting insights for designing and positioning textile-based strain sensors in wearable systems for scapular movements monitoring.

A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.

Use of wearable systems for the detection of chest-abdominal wall movement aimed at respiratory monitoring in sport: a scoping review on available data

There is a significant request for wearable systems for vital signs and athletic performance monitoring during sport practice, both in professional and non-professional fields. Respiratory rate is a rather neglected parameter in this field, but several studies show that it is a strong marker of physical exertion. The aim of the present scoping review is to evaluate the number and kind of existing studies on wearable technologies for the analysis of the chest wall movement for respiratory monitoring in sport and fitness. The review included studies investigating the use of contact-based wearable techniques for the detection of chest wall movement for respiratory monitoring during professional or amateur sport, during fitness and physical activity. The search was conducted on PubMed/Medline, Scopus and Google Scholar electronic databases using keywords. Data extracted were entered into a Microsoft Excel spreadsheet by the leading author and then double-checked by the second author. A total of 25 descriptive studies met the inclusion criteria. Few studies on small number of athletes were found, technologies were often evaluated without a reference system, data on participants are sometimes missing. To date, we are not able to draw conclusions on which is the best and most reliable device to use during sport practice.

Advanced Nanomaterials, Printing Processes, and Applications for Flexible Hybrid Electronics

Recent advances in nanomaterial preparation and printing technologies provide unique opportunities to develop flexible hybrid electronics (FHE) for various healthcare applications. Unlike the costly, multi-step, and error-prone cleanroom-based nano-microfabrication, the printing of nanomaterials offers advantages, including cost-effectiveness, high-throughput, reliability, and scalability. Here, this review summarizes the most up-to-date nanomaterials, methods of nanomaterial printing, and system integrations to fabricate advanced FHE in wearable and implantable applications. Detailed strategies to enhance the resolution, uniformity, flexibility, and durability of nanomaterial printing are summarized. We discuss the sensitivity, functionality, and performance of recently reported printed electronics with application areas in wearable sensors, prosthetics, and health monitoring implantable systems. Collectively, the main contribution of this paper is in the summary of the essential requirements of material properties, mechanisms for printed sensors, and electronics.

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband (UWB) and intrabody communication (IBC) have been the subject of intensive research in recent years due to their promising characteristics as means for short-range, low-power, and low-data-rate wireless interfaces for interconnection of various sensors and devices placed on, inside, or in the close vicinity of the human body. The need for safe and standardized solutions has resulted in the development of two relevant standards, IEEE 802.15.4 (for UWB) and IEEE 802.15.6 (for UWB and IBC), respectively. This paper presents an in-depth overview of recent studies and advances in the field of application of UWB and IBC technologies for wireless body sensor communication systems.

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Biofeedback Systems for Gait Rehabilitation of Individuals with Lower-Limb Amputation: A Systematic Review

Individuals with lower-limb amputation often have gait deficits and diminished mobility function. Biofeedback systems have the potential to improve gait rehabilitation outcomes. Research on biofeedback has steadily increased in recent decades, representing the growing interest toward this topic. This systematic review highlights the methodological designs, main technical and clinical challenges, and evidence relating to the effectiveness of biofeedback systems for gait rehabilitation. This review provides insights for developing an effective, robust, and user-friendly wearable biofeedback system. The literature search was conducted on six databases and 31 full-text articles were included in this review. Most studies found biofeedback to be effective in improving gait. Biofeedback was most commonly concurrently provided and related to limb loading and symmetry ratios for stance or step time. Visual feedback was the most used modality, followed by auditory and haptic. Biofeedback must not be obtrusive and ideally provide a level of enjoyment to the user. Biofeedback appears to be most effective during the early stages of rehabilitation but presents some usability challenges when applied to the elderly. More research is needed on younger populations and higher amputation levels, understanding retention as well as the relationship between training intensity and performance.

Requirement Specification and Modeling a Wearable Smart Blanket System for Monitoring Patients in Ambulance

Background:

Nowadays, the role of smart systems and developed tools such as wearable systems for monitoring the patients and controlling their conditions consistently has increased significantly. The present research sought to identify the factors which are essential for designing a wearable smart blanket system and modeling the proposed systems.

Methods:

To this aim, the requirements for creating the proposed system in ambulance were described after determining the features related to wearable systems by conducting on a comparative study. First, some studies were performed to identify the wearable system development. Then, the elicited questionnaire was given to the physicians and medical informatics specialists. Finally, the extracted requirements were implemented for modeling a smart blanket system.

Results:

Based on the results, the wearable smart blanket system includes some specific characteristics such as monitoring the important signs, communicating with the surroundings, processing the signals instantly, and storing all important signs. In addition, they should involve some nonfunctional characteristics such as easy installment and function, interactivity, error fault tolerance, low energy consumption, and the accuracy of sign stability. Then, based on the requirements and data elements extracted from the questionnaire, the system was modeled as a detailed design of the proposed technical blanket system. Based on the results, the architecture of the designed system could provide expected scenarios by using the Active Review for Intermediate Design-oriented scenario-based evaluation method.

Conclusion:

Today, smart systems and tools have considerably developed in terms of monitoring the patients and controlling their conditions. Therefore, wearable systems can be implemented for monitoring the health status of patients in ambulance.

Cardio-Respiratory Monitoring in Archery Using a Smart Textile Based on Flexible Fiber Bragg Grating Sensors

In precision sports, the control of breathing and heart rate is crucial to help the body to remain stable in the shooting position. To improve stability, archers try to adopt similar breathing patterns and to have a low heartbeat during each shot. We proposed an easy-to-use and unobtrusive smart textile (ST) which is able to detect chest wall excursions due to breathing and heart beating. The sensing part is based on two FBGs housed into a soft polymer matrix to optimize the adherence to the chest wall and the system robustness. The ST was assessed on volunteers to figure out its performance in the estimation of respiratory frequency (f_R) and heart rate (HR). Then, the system was tested on two archers during four shooting sessions. This is the first study to monitor cardio-respiratory activity on archers during shooting. The good performance of the ST is supported by the low mean absolute percentage error for f_R and HR estimation (≤1.97% and ≤5.74%, respectively), calculated with respect to reference signals (flow sensor for f_R, photopletismography sensor for HR). Moreover, results showed the capability of the ST to estimate f_R and HR during different phases of shooting action. The promising results motivate future investigations to speculate about the influence of f_R and HR on archers' performance.

N-Type Bismuth Telluride Nanocomposite Materials Optimization for Thermoelectric Generators in Wearable Applications

Thermoelectric materials could play a crucial role in the future of wearable electronic devices. They can continuously generate electricity from body heat. For efficient operation in wearable systems, in addition to a high thermoelectric figure of merit, zT, the thermoelectric material must have low thermal conductivity and a high Seebeck coefficient. In this study, we successfully synthesized high-performance nanocomposites of n-type Bi₂Te_2.7Se_0.3, optimized especially for body heat harvesting and power generation applications. Different techniques such as dopant optimization, glass inclusion, microwave radiation in a single mode microwave cavity, and sintering conditions were used to optimize the temperature-dependent thermoelectric properties of Bi₂Te_2.7Se_0.3. The effects of these techniques were studied and compared with each other. A room temperature thermal conductivity as low as 0.65 W/mK and high Seebeck coefficient of −297 μV/K were obtained for a wearable application, while maintaining a high thermoelectric figure of merit, zT, of 0.87 and an average zT of 0.82 over the entire temperature range of 25 °C to 225 °C, which makes the material appropriate for a variety of power generation applications.

Graphene-Graphite Polyurethane Composite Based High-Energy Density Flexible Supercapacitors

Energy autonomy is critical for wearable and portable systems and to this end storage devices with high-energy density are needed. This work presents high-energy density flexible supercapacitors (SCs), showing three times the energy density than similar type of SCs reported in the literature. The graphene-graphite polyurethane (GPU) composite based SCs have maximum energy and power densities of 10.22 µWh cm^-2 and 11.15 mW cm^-2, respectively, at a current density of 10 mA cm^-2 and operating voltage of 2.25 V (considering the IR drop). The significant gain in the performance of SCs is due to excellent electroactive surface per unit area (surface roughness 97.6 nm) of GPU composite and high electrical conductivity (0.318 S cm^-1). The fabricated SCs show stable response for more than 15 000 charging/discharging cycles at current densities of 10 mA cm^-2 and operating voltage of 2.5 V (without considering the IR drop). The developed SCs are tested as energy storage devices for wide applications, namely: a) solar-powered energy-packs to operate 84 light-emitting diodes (LEDs) for more than a minute and to drive the actuators of a prosthetic limb; b) powering high-torque motors; and c) wristband for wearable sensors.

Quality Assessment of Single-Channel EEG for Wearable Devices

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.

Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies

Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

Defining Requirements and Related Methods for Designing Sensorized Garments

Designing smart garments has strong interdisciplinary implications, specifically related to user and technical requirements, but also because of the very different applications they have: medicine, sport and fitness, lifestyle monitoring, workplace and job conditions analysis, etc. This paper aims to discuss some user, textile, and technical issues to be faced in sensorized clothes development. In relation to the user, the main requirements are anthropometric, gender-related, and aesthetical. In terms of these requirements, the user's age, the target application, and fashion trends cannot be ignored, because they determine the compliance with the wearable system. Regarding textile requirements, functional factors-also influencing user comfort-are elasticity and washability, while more technical properties are the stability of the chemical agents' effects for preserving the sensors' efficacy and reliability, and assuring the proper duration of the product for the complete life cycle. From the technical side, the physiological issues are the most important: skin conductance, tolerance, irritation, and the effect of sweat and perspiration are key factors for reliable sensing. Other technical features such as battery size and duration, and the form factor of the sensor collector, should be considered, as they affect aesthetical requirements, which have proven to be crucial, as well as comfort and wearability.

Body-monitoring and health supervision by means of optical fiber-based sensing systems in medical textiles

Long-term monitoring with optical fibers has moved into the focus of attention due to the applicability for medical measurements. Within this Review, setups of flexible, unobtrusive body-monitoring systems based on optical fibers and the respective measured vital parameters are in focus. Optical principles are discussed as well as the interaction of light with tissue. Optical fiber-based sensors that are already used in first trials are primarily selected for the section on possible applications. These medical textiles include the supervision of respiration, cardiac output, blood pressure, blood flow and its saturation with hemoglobin as well as oxygen, pressure, shear stress, mobility, gait, temperature, and electrolyte balance. The implementation of these sensor concepts prompts the development of wearable smart textiles. Thus, current sensing techniques and possibilities within photonic textiles are reviewed leading to multiparameter designs. Evaluation of these designs should show the great potential of optical fibers for the introduction into textiles especially due to the benefit of immunity to electromagnetic radiation. Still, further improvement of the signal-to-noise ratio is often necessary to develop a commercial monitoring system.