Wearable sensors and systems. From enabling technology to clinical applications

Future of magnetic sensors applications in early prediction of cardiac health status

The evolution of health monitoring technologies has highlighted the need for accurate and reliable sensors, particularly in the context of cardiac health. This review examines the potential of magnetic sensors as a superior alternative to optical sensors for the early prediction of cardiac health status. Optical sensors face significant challenges, especially for individuals with darker skin tones, where increased light absorption adversely affects measurement accuracy. Additionally, issues such as sensor-skin coupling and motion artifacts further compromise the performance of optical devices. In contrast, magnetic sensors offer a compelling solution by providing consistent readings irrespective of skin tone, thereby enhancing inclusivity in health monitoring. These sensors leverage magnetic fields, which do not rely on light penetration, allowing for improved coupling with the skin's surface and maintaining accuracy during motion. This paper discusses recent advancements in magnetic sensor technology and their implications for cardiac health applications, emphasizing the potential for increased accuracy and reliability in predicting cardiac outcomes. As healthcare shifts toward more personalized and precise monitoring solutions, magnetic sensors emerge as a promising frontier, addressing critical challenges in current health status prediction methods. By focusing on these innovative technologies, we aim to contribute to the ongoing discourse on enhancing cardiac health monitoring and fostering more equitable healthcare solutions.

Investigation and Validation of New Heart Rate Measurement Sites for Wearable Technologies

A recent focus has been on developing wearable health solutions that allow users to seamlessly track their health metrics during their daily activities, providing convenient and continuous access to vital physiological data. This work investigates a heart rate (HR) monitoring system and compares the HR measurement from two potential sites for foot wearable technologies. The proposed system used a commercially available photoplethysmography sensor (PPG), microcontroller, Bluetooth module, and mobile phone application. HR measurements were obtained from two anatomical sites, i.e., the dorsalis pedis artery (DPA) and the posterior tibial artery (PTA), and compared to readings from the Apple Smartwatch during standing and walking tasks. The system was validated on twenty healthy volunteers, employing ANOVA and Bland-Altman analysis to assess the accuracy and consistency of the HR measurements. During the standing test, the Bland-Altman analysis showed a mean difference of 0.08 bpm for the DPA compared to a smaller mean difference of 0.069 bpm for the PTA. On the other hand, the walking test showed a mean difference of 0.255 bpm and -0.06 bpm for the DPA and PTA, respectively. These results showed a high level of agreement between the HR measurements collected at the foot with the smartwatch measurements, with superiority for the HR measurements collected at the PTA.

Neuro-Nutrition and Exercise Synergy: Exploring the Bioengineering of Cognitive Enhancement and Mental Health Optimization

The interplay between nutrition, physical activity, and mental health has emerged as a frontier in bioengineering research, offering innovative pathways for enhancing cognitive function and psychological resilience. This review explores the neurobiological mechanisms underlying the synergistic effects of tailored nutritional strategies and exercise interventions on brain health and mental well-being. Key topics include the role of micronutrients and macronutrients in modulating neurogenesis and synaptic plasticity, the impact of exercise-induced myokines and neurotrophins on cognitive enhancement, and the integration of wearable bioelectronics for personalized monitoring and optimization. By bridging the disciplines of nutrition, psychology, and sports science with cutting-edge bioengineering, this review highlights translational opportunities for developing targeted interventions that advance mental health outcomes. These insights are particularly relevant for addressing global challenges such as stress, anxiety, and neurodegenerative diseases. The article concludes with a roadmap for future research, emphasizing the potential of bioengineered solutions to revolutionize preventive and therapeutic strategies in mental health care.

Can summary measures of magnitude and structure of trunk movement variability differentiate between people with and without chronic low back pain?

BACKGROUND

There is inconsistent evidence suggesting that people with chronic low back pain may differ in variability of repeated trunk movements compared to people without chronic low back pain. These inconsistencies may be due to low reliability and task dependence of movement variability measures, which can be addressed using multiple movement tasks and summary measures.

METHODS

Participants with and without chronic low back pain were recruited. Measurement sessions involved 30 repetitions of movements in the sagittal, transverse, and combined planes. Kinematics of the pelvis, thorax, and lumbar spine were estimated using inertial sensors placed on the sacrum and thorax. Magnitude of movement variability was quantified as the mean standard deviation of Euler angles for the thorax, pelvis, and lumbar spine across normalized cycles for each movement axes, resulting in 27 variables. Additionally, structure of variability was assessed using Lyapunov exponents for local dynamic stability, yielding 9 additional variables. Principal Component Analysis reduced the dimensionality of each variability measure (magnitude and structure). Stepwise logistic regression with principal component scores tested for differences between groups.

FINDINGS

In the magnitude of variability analysis, four principal components were retained. The first two principal components significantly differentiated between people with low back pain and controls, accounting for 32.5 % and 14 % of the total variance, respectively. In the structure of variability analysis, no principal components were found to significantly contribute to differentiating between the two groups.

INTERPRETATION

Summary measures of the magnitude, but not the structure, of trunk movement variability differentiated between people with and without chronic low back pain.

CLINICAL TRIAL

NCT02059317, CPP: 2013.11.09bis Sud Méditerranée III, N° RCB: 2013-A01379-36.

Advanced Wearable Sensors Technologies for Healthcare Monitoring

Wearable sensor technologies are rapidly evolving and expanding their reach into critical wellness and healthcare applications [...].

Wearable Sensors and Motion Analysis for Neurological Patient Support

This work discusses the state of the art and challenges in using wearable sensors for the monitoring of neurological patients. The authors share their experience from their participation in numerous projects, ranging from drug trials to rehabilitation intervention assessment, and identify the obstacles in the way of the integrated adoption of wearable sensors in clinical and rehabilitation practices for neurological patients. Several highly promising developments are outlined and analyzed. It is considered that intelligent textiles are an attractive option, as they offer an esthetic outlook to and positive interaction with their users.

An Embedded Electromyogram Signal Acquisition Device

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human-computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human-computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.

Concurrent Validity of the Ergotex Device for Measuring Low Back Posture

Highlighting the crucial role of monitoring and quantifying lumbopelvic rhythm for spinal curvature, the Ergotex IMU, a portable, lightweight, cost-effective, and energy-efficient technology, has been specifically designed for the pelvic and lumbar area. This study investigates the concurrent validity of the Ergotex device in measuring sagittal pelvic tilt angle. We utilized an observational, repeated measures design with healthy adult males (mean age: 39.3 ± 7.6 y, body mass: 82.2 ± 13.0 kg, body height: 179 ± 8 cm), comparing Ergotex with a 3D optical tracking system. Participants performed pelvic tilt movements in anterior, neutral, and posterior conditions. Statistical analysis included paired samples t-tests, Bland-Altman plots, and regression analysis. The findings show minimal systematic error (0.08° overall) and high agreement between the Ergotex and optical tracking, with most data points falling within limits of agreement of Bland-Altman plots (around ±2°). Significant differences were observed only in the anterior condition (0.35°, p < 0.05), with trivial effect sizes (ES = 0.08), indicating that these differences may not be clinically meaningful. The high Pearson's correlation coefficients across conditions underscore a robust linear relationship between devices (r > 0.9 for all conditions). Regression analysis showed a standard error of estimate (SEE) of 1.1° with small effect (standardized SEE < 0.26 for all conditions), meaning that the expected average deviation from the true value is around 1°. These findings validate the Ergotex as an effective, portable, and cost-efficient tool for assessing sagittal pelvic tilt, with practical implications in clinical and sports settings where traditional methods might be impractical or costly.

Exercise and gait/movement analyses in treatment and diagnosis of Parkinson's Disease

Cardinal motor symptoms in Parkinson's disease (PD) include bradykinesia, rest tremor and/or rigidity. This symptomatology can additionally encompass abnormal gait, balance and postural patterns at advanced stages of the disease. Besides pharmacological and surgical therapies, physical exercise represents an important strategy for the management of these advanced impairments. Traditionally, diagnosis and classification of such abnormalities have relied on partially subjective evaluations performed by neurologists during short and temporally scattered hospital appointments. Emerging sports medical methods, including wearable sensor-based movement assessment and computational-statistical analysis, are paving the way for more objective and systematic diagnoses in everyday life conditions. These approaches hold promise to facilitate customizing clinical trials to specific PD groups, as well as personalizing neuromodulation therapies and exercise prescriptions for each individual, remotely and regularly, according to disease progression or specific motor symptoms. We aim to summarize exercise benefits for PD with a specific emphasis on gait and balance deficits, and to provide an overview of recent advances in movement analysis approaches, notably from the sports science community, with value for diagnosis and prognosis. Although such techniques are becoming increasingly available, their standardization and optimization for clinical purposes is critically missing, especially in their translation to complex neurodegenerative disorders such as PD. We highlight the importance of integrating state-of-the-art gait and movement analysis approaches, in combination with other motor, electrophysiological or neural biomarkers, to improve the understanding of the diversity of PD phenotypes, their response to therapies and the dynamics of their disease progression.

Effects of heat and personal protective equipment on thermal strain in healthcare workers: part B-application of wearable sensors to observe heat strain among healthcare workers under controlled conditions

PURPOSE

As climate change accelerates, healthcare workers (HCW) are expected to be more frequently exposed to heat at work. Heat stress can be exacerbated by physical activity and unfavorable working requirements, such as wearing personal protective equipment (PPE). Thus, understanding its potential negative effects on HCW´s health and working performance is becoming crucial. Using wearable sensors, this study investigated the physiological effects of heat stress due to HCW-related activities.

METHODS

Eighteen participants performed four experimental sessions in a controlled climatic environment following a standardized protocol. The conditions were (a) 22 °C, (b) 22 °C and PPE, (c) 27 °C and (d) 27 °C and PPE. An ear sensor (body temperature, heart rate) and a skin sensor (skin temperature) were used to record the participants´ physiological parameters.

RESULTS

Heat and PPE had a significant effect on the measured physiological parameters. When wearing PPE, the median participants' body temperature was 0.1 °C higher compared to not wearing PPE. At 27 °C, the median body temperature was 0.5 °C higher than at 22 °C. For median skin temperature, wearing PPE resulted in a 0.4 °C increase and higher temperatures in a 1.0 °C increase. An increase in median heart rate was also observed for PPE (+ 2/min) and heat (+ 3/min).

CONCLUSION

Long-term health and productivity risks can be further aggravated by the predicted temperature rise due to climate change. Further physiological studies with a well-designed intervention are needed to strengthen the evidence for developing comprehensive policies to protect workers in the healthcare sector.

User Perspectives of Geriatric German Patients on Smart Sensor Technology in Healthcare

With consideration of the progressing aging of our societies, the introduction of smart sensor technology can contribute to the improvement of healthcare for older patients and to reductions of the costs of care. From the clinical and medico-ethical points of view, the advantages of smart sensor technology are copious. However, any ethical evaluation of an introduction of a new technology in medical practice requires an inclusion of patients' perspectives and their assessments. We have conducted qualitative, semi-structured, exploratory interviews with 11 older patients in order to gain their subjective opinions on the use of smart sensor devices for rehabilitation purposes. The interviews were analyzed using methods of qualitative content and thematic analyses. In our analysis, we have focused on ethical aspects of adoption of this technology in clinical practice. Most of the interviewees expressed their trust in this technology, foremost because of its accuracy. Several respondents stated apprehension that the use of smart sensors will lead to a change in the patient-healthcare professional relationship. Regarding costs of introduction of smart sensors into healthcare, interviewees were divided between health insurance bearing the costs and individual participation in corresponding costs. Most interviewees had no concerns about the protection of their privacy or personal information. Considering these results, improvement of users' technology literacy regarding possible threats connected with putting smart sensors into clinical practice is a precondition to any individual application of smart sensors. This should occur in the form of extended and well-designed patient information adapted to individual levels of understanding. Moreover, application of smart sensors needs to be accompanied with careful anamnesis of patient's needs, life goals, capabilities, and concerns.

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3-85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4-72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease

Objective

There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables.

Methods

A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n  =  15) and persons living with neurodegenerative disease (NDD) (n  =  25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7-10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status.

Results

Participants (n  =  40) were 60% female (median 72 (60-87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility.

Conclusions

The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.

Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.

A Wearable Flexible Acceleration Sensor for Monitoring Human Motion

Skin-inspired flexible wearable acceleration sensors attract much attention due to their advantages of portability, personalized and comfortable experience, and potential application in healthcare monitoring, human-machine interfaces, artificial intelligence, and physical sports performance evaluation. This paper presents a flexible wearable acceleration sensor for monitoring human motion by introducing the island-bridge configuration and serpentine interconnects. Compared with traditional wearable accelerometers, the flexible accelerometer proposed in this paper improves the wearing comfort while reducing the cost of the device. Simulation and experiments under bending, stretching, and torsion conditions demonstrate that the flexible performance of the flexible acceleration sensor can meet the needs of monitoring the daily movement of the human body, and it can work normally under various conditions. The measurement accuracy of the flexible acceleration sensor is verified by comparing it with the data of the commercial acceleration sensor. The flexible acceleration sensor can measure the acceleration and the angular velocity of the human body with six degrees of freedom and recognize the gesture and motion features according to the acceleration characteristics. The presented flexible accelerometers provide great potential in recognizing the motion features that are critical for healthcare monitoring and physical sports performance evaluation.

Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network

With the rapid development of the Internet, various electronic products based on computer vision play an increasingly important role in people's daily lives. As one of the important topics of computer vision, human action recognition has become the main research hotspot in this field in recent years. The human motion recognition algorithm based on the convolutional neural network can realize the automatic extraction and learning of human motion features and achieve good classification performance. However, deep convolutional neural networks usually have a large number of layers, a large number of parameters, and a large memory footprint, while embedded wearable devices have limited memory space. Based on the traditional cross-entropy error-based training mode, the parameters of all hidden layers must be kept in memory and cannot be released until the end of forward and reverse error propagation. As a result, the memory used to store the parameters of the hidden layer cannot be released and reused, and the memory utilization efficiency is low, which leads to the backhaul locking problem, limiting the deployment and execution of deep convolutional neural networks on wearable sensor devices. Based on this, this topic designs a local error convolutional neural network model for human motion recognition tasks. Compared with the traditional global error, the local error constructed in this paper can train the convolutional neural network layer by layer, and the parameters of each layer can be trained independently according to the local error and does not depend on the gradient propagation of adjacent upper and lower layers. As a result, the memory used to store all hidden layer parameters can be released in advance without waiting for the end of forward and backward propagation, avoiding the problem of backhaul locking, and improving the memory utilization of convolutional neural networks deployed on embedded wearable devices.

Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature

Background

Accelerometery is commonly used to estimate physical activity, sleep, and sedentary behavior. In free-living conditions, periods of device removal (non-wear) can lead to misclassification of behavior with consequences for research outcomes and clinical decision making. Common methods for non-wear detection are limited by data transformations (e.g., activity counts) or algorithm parameters such as minimum durations or absolute temperature thresholds that risk over- or under-estimating non-wear time. This study aimed to advance non-wear detection methods by integrating a ‘rate-of-change’ criterion for temperature into a combined temperature-acceleration algorithm.

Methods

Data were from 39 participants with neurodegenerative disease (36% female; age: 45–83 years) who wore a tri-axial accelerometer (GENEActiv) on their wrist 24-h per day for 7-days as part of a multi-sensor protocol. The reference dataset was derived from visual inspection conducted by two expert analysts. Linear regression was used to establish temperature rate-of-change as a criterion for non-wear detection. A classification and regression tree (CART) decision tree classifier determined optimal parameters separately for non-wear start and end detection. Classifiers were trained using data from 15 participants (38.5%). Outputs from the CART analysis were supplemented based on edge cases and published parameters.

Results

The dataset included 186 non-wear periods (85.5% < 60 min). Temperature rate-of-change over the first five minutes of non-wear was − 0.40 ± 0.17 °C/minute and 0.36 ± 0.21 °C/minute for the first five minutes following device donning. Performance of the DETACH (DEvice Temperature and Accelerometer CHange) algorithm was improved compared to existing algorithms with recall of 0.942 (95% CI 0.883 to 1.0), precision of 0.942 (95% CI 0.844 to 1.0), F1-Score of 0.942 (95% CI 0.880 to 1.0) and accuracy of 0.996 (0.994–1.000).

Conclusion

The DETACH algorithm accurately detected non-wear intervals as short as five minutes; improving non-wear classification relative to current interval-based methods. Using temperature rate-of-change combined with acceleration results in a robust algorithm appropriate for use across different temperature ranges and settings. The ability to detect short non-wear periods is particularly relevant to free-living scenarios where brief but frequent removals occur, and for clinical application where misclassification of behavior may have important implications for healthcare decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01633-6.

Ergonomic Assessment of Physical Load in Slovak Industry Using Wearable Technologies

The physical tasks of workers are demanding, particularly when performed long-term in unsuitable working position, with high frequency, heavy load, after injury, with developing damage of health or reduced performance due to advanced age. Work-related musculoskeletal disorders (WMSDs) result from overuse or develop over time. Work activities, which are frequent and repetitive, or activities with awkward postures, cause disorders that may be painful during work or at rest. There is a new technology in the market, occupational exoskeletons, which have the prerequisites for minimizing the negative consequences of workload on WMSDs. We provided pilot quantitative measurements of the ergonomic risk at one selected workplace in a Slovak automotive company with four different workers to prove our methodology using wearable wireless multi-sensor systems Captiv and Actigraph. At first, the test was performed in standard conditions without an exoskeleton. The unacceptable physical load was identified in considerable evaluated body areas—neck, hip, and shoulder. Next, the passive chair exoskeleton Chairless Chair 2.0 was used in trials as an ergonomic measure. Our intention was to determine whether an exoskeleton would be an effective tool for optimizing the workload in selected workplaces and whether the proposed unique quantitative measurement system would give reliable and quick results.

Application of Wearable Sensors Technology for Lumbar Spine Kinematic Measurements during Daily Activities following Microdiscectomy Due to Severe Sciatica

Simple Summary

The recurrence rate after lumbar spine disc surgeries is estimated to be 5–15%. Lumbar spine flexion of more than 10° is mentioned in the literature as the most harmful load to the operated disc level that could lead to recurrence during the first six postoperative weeks. The purpose of this study is to quantify flexions during daily living following such surgeries, for six weeks postoperatively, using wearable sensors technology. These data determine the patients’ kinematic pattern, reflecting a high-risk factor for pathology recurrence. The operated patients were measured to have 30% normal lumbar motion after the first postoperative week, while they were restored to almost 75% at the end of the sixth, respectively. Further in vitro studies should be carried out using these data to identify if such kinematic patterns could lead to pathology recurrence.

Abstract

Background: The recurrence rate of lumbar spine microdiscectomies (rLSMs) is estimated to be 5–15%. Lumbar spine flexion (LSF) of more than 10° is mentioned as the most harmful load to the intervertebral disc that could lead to recurrence during the first six postoperative weeks. The purpose of this study is to quantify LSFs, following LSM, at the period of six weeks postoperatively. Methods: LSFs were recorded during the daily activities of 69 subjects for 24 h twice per week, using Inertial Measurement Units (IMU). Results: The mean number of more than 10 degrees of LSFs per hour were: 41.3/h during the 1st postoperative week (P.W.) (29.9% healthy subjects-H.S.), 2nd P.W. 60.1/h (43.5% H.S.), 3rd P.W. 74.2/h (53.7% H.S.), 4th P.W. 82.9/h (60% H.S.), 5th P.W. 97.3/h (70.4% H.S.) and 6th P.W. 105.5/h (76.4% H.S.). Conclusions: LSFs constitute important risk factors for rLDH. Our study records the lumbar spine kinematic pattern of such patients for the first time during their daily activities. Patients’ data report less sagittal plane movements than healthy subjects. In vitro studies should be carried out, replicating our results to identify if such a kinematic pattern could cause rLDH. Furthermore, IMU biofeedback capabilities could protect patients from such harmful movements.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

Characterization of Ag/AgCl Dry Electrodes for Wearable Electrophysiological Sensing

With the rising need for on-body biometric sensing, the development of wearable electrophysiological sensors has been faster than ever. Surface electrodes placed on the skin need to be robust in order to measure biopotentials from the body reliably and comfortable for extended wearability. The electrical stability of nonpolarizable silver/silver chloride (Ag/AgCl) and its low-cost, commercial production have made these electrodes ubiquitous health sensors in the clinical environment, where wet gels and long wires are accommodated by patient immobility. However, smaller, dry electrodes with wireless acquisition are essential for truly wearable, continuous health sensing. Currently, techniques for the robust fabrication of custom Ag/AgCl electrodes are lacking. Here, we present three methods for the fabrication of Ag/AgCl electrodes: oxidizing Ag in a chlorine solution, electroplating Ag, and curing Ag/AgCl ink. Each of these methods is then used to create three different electrode shapes for wearable application. Bench-top and on-body evaluation of the electrode techniques was achieved by electrochemical impedance spectroscopy (EIS), calculation of variance in electrocardiogram (ECG) measurements, and analysis of auditory steady-state response (ASSR) measurement. Microstructures produced on the electrode by each fabrication technique were also investigated with scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The custom Ag/AgCl electrodes were found to be efficient in comparison with standard, commercial Ag/AgCl wet electrodes across all three of our presented techniques, with Ag/AgCl ink shown to be the better out of the three in bench-top and biometric recordings.

Advanced sensor technologies and the future of work

Exposure science is fundamental to the field of occupational safety and health. The measurement of worker exposures to hazardous agents informs effective workplace risk mitigation strategies. The modern era of occupational exposure measurement began with the invention of the personal sampling device, which is still widely used today in the practice of occupational hygiene. Newer direct-reading sensor devices are incorporating recent advances in transducers, nanomaterials, electronics miniaturization, portability, batteries with high-power density, wireless communication, energy-efficient microprocessing, and display technology to usher in a new era in exposure science. Commercial applications of new sensor technologies have led to a variety of health and lifestyle management devices for everyday life. These applications are also being investigated as tools to measure occupational and environmental exposures. As the next-generation placeable, wearable, and implantable sensor technologies move from the research laboratory to the workplace, their role in the future of work will be of increasing importance to employers, workers, and occupational safety and health researchers and practitioners. This commentary discusses some of the benefits and challenges of placeable, wearable, and implantable sensor technologies in the future of work.

Study the Electrical Properties of Surface Mount Device Integrated Silver Coated Vectran Yarn

Smart textiles have attracted huge attention due to their potential applications for ease of life. Recently, smart textiles have been produced by means of incorporation of electronic components onto/into conductive metallic yarns. The development, characterizations, and electro-mechanical testing of surface mounted electronic device (SMD) integrated E-yarns is still limited. There is a vulnerability to short circuits as non-filament conductive yarns have protruding fibers. It is important to determine the best construction method and study the factors that influence the textile properties of the base yarn. This paper investigated the effects of different external factors, namely, strain, solder pad size, temperature, abrasion, and washing on the electrical resistance of SMD integrated silver-coated Vectran (SCV) yarn. For this, a Vectran E-yarn was fabricated by integrating the SMD resistor into a SCV yarn by applying a vapor phase reflow soldering method. The results showed that the conductive gauge length, strain, overlap solder pad size, temperature, abrasion, and washing had a significant effect on the electrical resistance property of the SCV E-yarn. In addition, based on the experiment, the E-yarn made from SCV conductive thread and 68 Ω SMD resistor had the maximum electrical resistance and power of 72.16 Ω and 0.29 W per 0.31 m length. Therefore, the structure of this E-yarn is also expected to bring great benefits to manufacturing wearable conductive tracks and sensors.

Classification of Single-Axis Spinal Motion Using a Wearable System of Stretch Sensors for At-home Physical Therapy

Physical therapy (PT) has demonstrated therapeutic effectiveness for treating low back pain, a prevalent health condition. However, it is challenging to achieve such effectiveness through at-home PT without supervision of a therapist. Towards enabling realtime biofeedback for ensuring correct execution of PT exercises at home, we are building a wearable system that employs light-weight stretch sensors for estimating the spinal posture of a patient performing PT exercises. A basic task is to detect single-axis spinal motions from the sensor measurements. This work presents the design and evaluation of our approach for this task. Three subjects of different body shapes were recruited to wear the system and perform sequences of arbitrary single-axis spinal exercises. The collected data were used to train and test an SVM-based classification algorithm. Experimental results demonstrate that it is feasible to rely on only a small number of stretch sensors to estimate the spinal motion. The results also suggest the existence of strong inter-person variability and thus a practical system should include calibration for ensuring high accuracy.

Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease

BACKGROUND

Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD).

METHODS

Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance.

RESULTS

Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners.

CONCLUSION

A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.

Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages

With the development of mobile and wearable devices with biosensors, various healthcare services in our life have been recently introduced. A significant issue that arises supports the smart interface among bio-signals developed by different vendors and different languages. Despite its importance for convenient and effective development, however, it has been nearly unexplored. This paper focuses on the smart interface format among bio-signal data processing and mining algorithms implemented by different languages. We designed and implemented an advanced software structure where analysis algorithms implemented by different languages and tools would seem to work in one common environment, overcoming different developing language barriers. By presenting our design in this paper, we hope there will be much more chances for higher service-oriented developments utilizing bio-signals in the future.

Intelligent Platform Based on Smart PPE for Safety in Workplaces

It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, workers are as safe and protected as possible. Thanks to Smart Personal Protective Equipment (PPE) and wearable technologies, information about the workers and their environment can be extracted to reduce the rate of accidents and occupational illness, leading to a significant improvement. This article proposes an architecture that employs three pieces of PPE: a helmet, a bracelet and a belt, which process the collected information using artificial intelligence (AI) techniques through edge computing. The proposed system guarantees the workers’ safety and integrity through the early prediction and notification of anomalies detected in their environment. Models such as convolutional neural networks, long short-term memory, Gaussian Models were joined by interpreting the information with a graph, where different heuristics were used to weight the outputs as a whole, where finally a support vector machine weighted the votes of the models with an area under the curve of 0.81.

Parkinson's Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.

Non-Contact Heart-Rate Measurement Method Using Both Transmitted Wave Extraction and Wavelet Transform

Continuous monitoring of heart-rate is expected to lead to early detection of physical discomfort. In this study, we propose a non-contact heart-rate measurement method which can be used in an environment such as driver heart-rate monitoring with body movement. The method is based on the electric field strength transmitted through the human body that changes with the diastole and systole of the heart. Unlike conventional displacement detection of the skin surface, we attempted to capture changes in the internal structure of the human body by irradiating the human body with microwaves and acquiring microwaves that pass through the heart. We first estimated the electric field strength transmitted through the heart using three receiving sensors to reduce the body movement effect. Then we decomposed the estimated transmitted electric field using stationary wavelet transform to eliminate significant distortion due to body movement. As a result, we achieved an estimation accuracy of heart-rate as high as 98% in a verification experiment with normal body movement.

Parkinson's disease medication state and severity assessment based on coordination during walking

Walking is a complex motor function requiring coordination of all body parts. Parkinson's disease (PD) motor signs such as rigidity, bradykinesia, and impaired balance affect movements including walking. Here, we propose a computational method to objectively assess the effects of Parkinson's disease pathology on coordination between trunk, shoulder and limbs during the gait cycle to assess medication state and disease severity. Movements during a scripted walking task were extracted from wearable devices placed at six different body locations in participants with PD and healthy participants. Three-axis accelerometer data from each device was synchronized at the beginning of either left or right steps. Canonical templates of movements were then extracted from each body location. Movements projected on those templates created a reduced dimensionality space, where complex movements are represented as discrete values. These projections enabled us to relate the body coordination in people with PD to disease severity. Our results show that the velocity profile of the right wrist and right foot during right steps correlated with the participant's total score on the gold standard Unified Parkinson's Disease Rating Scale (UPRDS) with an r2 up to 0.46. Left-right symmetry of feet, trunk and wrists also correlated with the total UPDRS score with an r2 up to 0.3. In addition, we demonstrate that binary dopamine replacement therapy medication states (self-reported 'ON' or 'OFF') can be discriminated in PD participants. In conclusion, we showed that during walking, the movement of body parts individually and in coordination with one another changes in predictable ways that vary with disease severity and medication state.

Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

A treadmill was used to perform continuous walking tests in a limited space that can be covered by marker-based optical motion capture systems. Most treadmill-based gait data are analyzed based on gait cycle percentage. However, achieving continuous walking motion trajectories over time without time normalization is often required, even if tests are performed under treadmill walking conditions. This study presents a treadmill-to-overground mapping method of optical marker trajectories for treadmill-based continuous gait analysis, by adopting a simple concept of virtual origin. The position vector from the backward moving virtual origin to a targeted marker within a limited walking volume is the same as the position vector from the fixed origin to the forward moving marker over the ground. With the proposed method, it is possible (i) to observe the change in physical quantity visually during the treadmill walking, and (ii) to obtain overground-mapped gait data for evaluating the accuracy of the inertial-measurement-unit-based trajectory estimation. The accuracy of the proposed method was verified from various treadmill walking tests, which showed that the total travel displacement error rate was 0.32% on average.

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.

Common-Mode Noise Reduction in Noncontact Biopotential Acquisition Circuit Based on Imbalance Cancellation of Electrode-Body Impedance

Biopotential sensing technology with electrodes has a great future in medical treatment and human—machine interface, whereas comfort and longevity are two significant problems during usage. Noncontact electrode is a promising alternative to achieve more comfortable and long term biopotential signal recordings than contact electrode. However, it could pick up a significantly higher level of common-mode (CM) noise, which is hardly solved with passive filtering. The impedance imbalance at the electrode-body interface is a limiting factor of this problem, which reduces the common mode rejection ratio (CMRR) of the amplifier. In this work, we firstly present two novel CM noise reduction circuit designs. The circuit designs are based on electrode-body impedance imbalance cancellation. We perform circuit analysis and circuit simulations to explain the principles of the two circuits, both of which showed effectiveness in CM noise rejection. Secondly, we proposed a practical approach to detect and monitor the electrode-body impedance imbalance change. Compared with the conventional approach, it has certain advantages in interference immunity, and good linearity for capacitance. Lastly, we show experimental evaluation results on one of the designs we proposed. The results indicated the validity and feasibility of the approach.

Blood pressure estimation system using human body communication-based electrocardiograph and photoplethysmography

In order to realise low-load cuffless and continuous blood pressure measurement in daily life, the authors developed a blood pressure estimation system combining human body communication-based wearable electrocardiograph and reflectance photoplethysmography. The principle is based on a relationship between the pulse arrive time and the systolic blood pressure. The pulse arrive time is the time period between the R-wave in electrocardiograph and peak of pulse wave. The greatest feature is the use of a human body communication-based electrocardiograph which can provide automatic synchronisation in time between the measured electrocardiograph and pulse wave signals to obtain the pulse arrive time so that no additional synchronisation circuit is required. Using this system, the authors measured the pulse arrive time from the electrocardiograph and pulse wave signals in real time, estimated the systolic blood pressure and compared the result with that measured by a cuff sphygmomanometer. The authors found that the root mean square error of the estimated blood pressure and the actual value measured using the cuff sphygmomanometer was 4.5 mmHg or less, and the correlation coefficient was >0.6 with a P value much <0.05. These results show the validity of the developed system for cuffless and continuous blood pressure estimation.

Modeling Fabric Movement for Future E-Textile Sensors

Studies with e-textile sensors embedded in garments are typically performed on static and controlled phantom models that do not reflect the dynamic nature of wearables. Instead, our objective was to understand the noise e-textile sensors would experience during real-world scenarios. Three types of sleeves, made of loose, tight, and stretchy fabrics, were applied to a phantom arm, and the corresponding fabric movement was measured in three dimensions using physical markers and image-processing software. Our results showed that the stretchy fabrics allowed for the most consistent and predictable clothing-movement (average displacement of up to −2.3 ± 0.1 cm), followed by tight fabrics (up to −4.7 ± 0.2 cm), and loose fabrics (up to −3.6 ± 1.0 cm). In addition, the results demonstrated better performance of higher elasticity (average displacement of up to −2.3 ± 0.1 cm) over lower elasticity (average displacement of up to −3.8 ± 0.3 cm) stretchy fabrics. For a case study with an e-textile sensor that relies on wearable loops to monitor joint flexion, our modeling indicated errors as high as 65.7° for stretchy fabric with higher elasticity. The results from this study can (a) help quantify errors of e-textile sensors operating “in-the-wild,” (b) inform decisions regarding the optimal type of clothing-material used, and (c) ultimately empower studies on noise calibration for diverse e-textile sensing applications.

Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study

Background

New wearable devices (for example, AliveCor or Zio patch) offer promise in detecting arrhythmia and monitoring cardiac health status, among other clinically useful parameters in older adults. However, the clinical utility and usability from the perspectives of clinicians is largely unexplored.

Objective

This study aimed to explore clinician perspectives on the use of wearable cardiac monitoring technology for older adults.

Methods

A descriptive qualitative study was conducted using semistructured focus group interviews. Clinicians were recruited through purposive sampling of physicians, nurses, and allied health staff working in 3 tertiary-level hospitals. Verbatim transcripts were analyzed using thematic content analysis to identify themes.

Results

Clinicians representing physicians, nurses, and allied health staff working in 3 tertiary-level hospitals completed 4 focus group interviews between May 2019 and July 2019. There were 50 participants (28 men and 22 women), including cardiologists, geriatricians, nurses, and allied health staff. The focus groups generated the following 3 overarching, interrelated themes: (1) the current state of play, understanding the perceived challenges of patient cardiac monitoring in hospitals, (2) priorities in cardiac monitoring, what parameters new technologies should measure, and (3) cardiac monitoring of the future, “the ideal device.”

Conclusions

There remain pitfalls related to the design of wearable cardiac technology for older adults that present clinical challenges. These pitfalls and challenges likely negatively impact the uptake of wearable cardiac monitoring in routine clinical care. Partnering with clinicians and patients in the co-design of new wearable cardiac monitoring technologies is critical to optimize the use of these devices and their uptake in clinical care.

Health technology identities and self. Patients' appropriation of an assistive device for self-management of chronic illness

In recent years, assistive technologies have gained acceptance as tools for supporting chronically ill patients in achieving improvements in physical activity. However, various healthcare and sociological studies show contradicting results regarding the physical and social impact of using such devices. This paper explores real-time user appropriation of an assistive monitoring/tracking device, the pedometer, in a healthcare intervention, with a particular focus on the technology identities users attribute to the pedometer. The study site was a rehabilitation programme at a local Danish health centre supporting patients with chronic obstructive pulmonary disease. As part of this empirical study, six focus-group interviews were conducted with patients before and after they used pedometers. The analysis of respondents' accounts shows that monitoring devices become part of users' complex socio-technical ensembles in which the use of the device and its tracking of activity is constantly negotiated through experimentation with type and frequency of use; interpretation of knowledge and experience gained via the device; and negotiation of expectations, wellbeing, and the value of quantified knowledge for the management of chronic illness. On the basis of these findings the paper brings together and advances sociological scholarship on chronic illness, embodiment, the quantified self and technology adoption.

Using an unbiased symbolic movement representation to characterize Parkinson's disease states

Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.

Validation of Wearable Devices to Measure Energy Consumption

OBJECTIVES The purpose of this study is to verify how accurately wearable devices measure energy expenditure while walking outdoors.METHODS A total of 20 people, 10 healthy males and 10 females, participated in the study. After approval by the Institutional Review Board of Pusan National University, the experiment was conducted with the consent of the subject. All subjects wore four wearable devices (Fitbit Surge, Android Phone, iPhone, pedometer) and a portable gas analyzer simultaneously and walked on flat, downhill and uphill road respectively. All subjects repeated these experiments on each slope three times. The validity was verified through correlation analyses and paired <i>t</i>-test between the energy expenditure measured by the wearable devices and by a portable gas analyzer.RESULTS Under all three road slopes (flat, downhill, uphill road), the energy expenditure as measured by iPhone, Android phones, and pedometer significantly correlated with the portable gas analyzer. However, all three devices were significantly overestimated or underestimated the energy expenditure as compared to the gas analyzer under all three road slopes. Fitbit Surge did not correlate with the gas analyzer for measuring energy expenditure under any conditions, and significantly overestimated energy expenditure.CONCLUSION The validity of energy expenditure measurement during outdoor activities using wearable devices is still low, and more valid motion detection sensors and algorithms need to be developed.

Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

Background

Utilization of the widely used wearable sensor and smartphone technology for remote monitoring represents a healthcare breakthrough. This study aims to design a remote real-time monitoring system for multiple physiological parameters (electrocardiogram, heart rate, respiratory rate, blood oxygen saturation, and temperature) based on smartphones, considering high performance, autoalarm generation, warning transmission, and security through more than one method.

Methods

Data on monitoring parameters were acquired by the integrated circuits of wearable sensors and collected by an Arduino Mega 250 R3. The collected data were transmitted via a Wi-Fi interface to a smartphone. A patient application was developed to analyze, process, and display the data in numerical and graphical forms. The abnormality threshold values of parameters were identified and analyzed to generate an autoalarm in the system and transmitted with data to a doctor application via a third-generation (3G) mobile network and Wi-Fi. The performance of the proposed system was verified and evaluated. The proposed system was designed to meet main (sensing, processing, displaying, real-time transmission, autoalarm generation, and threshold value identification) and auxiliary requirements (compatibility, comfort, low power consumption and cost, small size, and suitability for ambulatory applications).

Results

System performance is reliable, with a sufficient average accuracy measurement (99.26%). The system demonstrates an average time delay of 14 s in transmitting data to a doctor application via Wi-Fi compared with an average time of 68 s via a 3G mobile network. The proposed system achieves low power consumption against time (4 h 21 m 30 s) and the main and auxiliary requirements for remotely monitoring multiple parameters simultaneously with secure data.

Conclusions

The proposed system can offer economic benefits for remotely monitoring patients living alone or in rural areas, thereby improving medical services, if manufactured in large quantities.

Classifying Diverse Physical Activities Using "Smart Garments"

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.