Wearable Devices for Sports: New Integrated Technologies Allow Coaches, Physicians, and Trainers to Better Understand the Physical Demands of Athletes in Real time

Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

BACKGROUND

In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete's day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain.

OBJECTIVE

This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction.

METHODS

Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews.

RESULTS

Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the "pre" stage of SIJ dysfunction, while 6 (29%) were identified as being at the "at" stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage.

CONCLUSIONS

SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.

Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim

Walking is one of the main activities of daily life and gait analysis can provide crucial data for the computation of biomechanics in many fields. In multiple applications, having reference data that include a variety of gait conditions could be useful for assessing walking performance. However, limited extensive reference data are available as many conditions cannot be easily tested experimentally. For this reason, a musculoskeletal model in OpenSim coupled with gait data (at seven different velocities) was used to simulate seven carried loads and all the combinations between the two parameters. The effects on lower limb biomechanics were measured with torque, power, and mechanical work. The results demonstrated that biomechanics was influenced by both speed and load. Our results expand the previous literature: in the majority of previous work, only a subset of the presented conditions was investigated. Moreover, our simulation approach provides comprehensive data that could be useful for applications in many areas, such as rehabilitation, orthopedics, medical care, and sports.

The Fundamentals and Applications of Wearable Sensor Devices in Sports Medicine: A Scoping Review

PURPOSE

To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine.

METHODS

A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research.

RESULTS

In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis.

CONCLUSIONS

WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research.

CLINICAL RELEVANCE

Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

Musculoskeletal Rehabilitation: New Perspectives in Postoperative Care Following Total Knee Arthroplasty Using an External Motion Sensor and a Smartphone Application for Remote Monitoring

BACKGROUND

The number of total knee replacements performed annually is steadily increasing. Parallel options for postoperative care are decreasing, which reduces patient satisfaction. External devices to support physical rehabilitation and health monitoring will improve patient satisfaction and postoperative care.

METHODS

In a prospective, international multicenter study, patients were asked to use an external motion sensor and a smartphone application during the postoperative course of primary total knee arthroplasty. The collected data were transferred to a data platform, allowing for the real-time evaluation of patient data.

RESULTS

In three participating centers, 98 patients were included. The general acceptance of using the sensor and app was high, with an overall compliance in study participation rate of up to 76%. The early results showed a significant improvement in the overall quality of life (p < 0.001) and significant reductions in pain (p < 0.01) and depression (p < 0.001).

CONCLUSIONS

The early results of this clinical and multicenter study emphasize that there is a high interest in and acceptance of digital solutions in patients' treatment pathways. Motion sensor and smartphone applications support patients in early rehabilitation.

Electric Double Layer Based Epidermal Electronics for Healthcare and Human-Machine Interface

Epidermal electronics, an emerging interdisciplinary field, is advancing the development of flexible devices that can seamlessly integrate with the skin. These devices, especially Electric Double Layer (EDL)-based sensors, overcome the limitations of conventional electronic devices, offering high sensitivity, rapid response, and excellent stability. Especially, Electric Double Layer (EDL)-based epidermal sensors show great potential in the application of wearable electronics to detect biological signals due to their high sensitivity, fast response, and excellent stability. The advantages can be attributed to the biocompatibility of the materials, the flexibility of the devices, and the large capacitance due to the EDL effect. Furthermore, we discuss the potential of EDL epidermal electronics as wearable sensors for health monitoring and wound healing. These devices can analyze various biofluids, offering real-time feedback on parameters like pH, temperature, glucose, lactate, and oxygen levels, which aids in accurate diagnosis and effective treatment. Beyond healthcare, we explore the role of EDL epidermal electronics in human-machine interaction, particularly their application in prosthetics and pressure-sensing robots. By mimicking the flexibility and sensitivity of human skin, these devices enhance the functionality and user experience of these systems. This review summarizes the latest advancements in EDL-based epidermal electronic devices, offering a perspective for future research in this rapidly evolving field.

Reliability of ADR Jumping Photocell: Comparison of Beam Cut at Forefoot and Midfoot

The ability to detect small changes in a vertical jump is crucial when data are used by sports science specialists to monitor their athletes. This study aimed to analyze the intrasession reliability of the ADR jumping photocell and the reliability relative to the position of the transmitter when it is located facing the phalanges of the foot (forefoot) or the metatarsal area (midfoot). A total of 12 female volleyball players performed 240 countermovement jumps (CMJ), alternating both methods. The intersession reliability was higher for the forefoot method (ICC = 0.96; CCC = 0.95; SEM = 1.15 cm; CV = 4.11%) than for the midfoot method (ICC = 0.85; CCC = 0.81; SEM = 3.68 cm; CV = 8.75%). Similarly, the sensitivity values were better for the forefoot method (SWC = 0.32) than for the midfoot method (SWC = 1.04). Significant differences were found between the methods (13.5 cm, p < 0.05, ES = 2.1) with low agreement (rs = 0.57; ICC = 0.49; CCC = 0.15; SEM = 4.7 cm) and heteroscedasticity was observed (r2 > 0.1). In conclusion, the ADR jumping photocell is shown to be a reliable tool for measuring CMJs. However, the reliability of the instrument can be influenced depending on the placement of the device. Comparing the two methods, the midfoot placement was less reliable as indicated by higher values of SEM and systematic error, and thus its use is not recommended.

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review

Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.

Evaluating the intra- and inter-day reliability of output measures for the VALD HumanTrak: dynamic movements and range of motion of the shoulder and hip with body armour

The HumanTrak captures human movement through markerless motion tracking and can be a crucial tool in military physical screening. Reliability was examined in eighteen healthy participants who completed shoulder and hip ROM, and dynamic tasks in three body armour conditions. Generally, for all conditions, good to excellent reliability was observed in shoulder abduction and flexion, hip abduction and adduction, and dynamic squats knee and hip flexion (ICC ≥ 0.75 excluding outliers). Shoulder adduction and hip flexion demonstrated moderate to excellent reliability (ICC ≥ 0.50). Shoulder and hip extension and the drop jump were unreliable (ICC: 0.10-0.94, 0.15-0.89, and 0.30-0.82, respectively) due to the large distribution of ICC scores. Tasks with ROM values ≥ 100° involving movement towards or perpendicular to the HumanTrak camera tended to have greater reliability than movements moving away from the camera and out of the perpendicular plane regardless if body armour was worn.Practitioner summary: The HumanTrak analyses ROM in a time-efficient manner in a military setting. This study established that shoulder abduction and adduction (no body armour) and shoulder, hip, and knee flexion were the most reliable measurement for all conditions. Further work is required for movements across different planes.Abbreviations: ROM: range of motion; NBA: no body armour; BA: unloaded body armour; BA9: body armour with 9 kg; RGB: red, green, blue; ICC: intra-class correlation; SEM: standard error of measurement; MDC: minimal detectable change: MSE: mean square error; r: pearson correlations; N: sample size.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during a protocol including the four swimming techniques. The proposed methodology took high inter- and intra-swimmer variability into account and was set up for the purpose of predicting eight swimming classes (the four swimming techniques, rest, wallpush, underwater, and turns) at four swimming velocities ranging from low to maximal. The overall F1-score of classification reached 0.96 with a temporal precision of 0.02 s. Lap times were directly computed from the classifier thanks to a high temporal precision and validated against a video gold standard. The mean absolute percentage error (MAPE) for this model against the video was 1.15%, 1%, and 4.07%, respectively, for starting lap times, middle lap times, and ending lap times. This model is a first step toward a powerful training assistant able to analyze swimmers with various levels of expertise in the context of in situ training monitoring.

Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running

Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the basis for computing inverse dynamics. Wearable technology can predict time−continuous GRFs during walking and running; however, the majority of GRF predictions examine level ground locomotion. The purpose of this manuscript was to predict vertical and anterior–posterior GRFs across different speeds and slopes. Eighteen recreationally active subjects ran on an instrumented treadmill while we collected GRFs and plantar pressure. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. We estimated GRFs using a set of linear models and a recurrent neural network, which used speed, slope, and plantar pressure as inputs. We also tested eliminating speed and slope as inputs. The recurrent neural network outperformed the linear model across all conditions, especially with the prediction of anterior–posterior GRFs. Eliminating speed and slope as model inputs had little effect on performance. We also demonstrate that subject−specific model training can reduce errors from 8% to 3%. With such low errors, researchers can use these wearable−based GRFs to understand running performance or injuries in real−world settings.

Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications

Wearable technologies are emerging as a useful tool with many different applications. While these devices are worn on the human body and can capture numerous data types, this literature review focuses specifically on wearable use for performance enhancement and risk assessment in industrial- and sports-related biomechanical applications. Wearable devices such as exoskeletons, inertial measurement units (IMUs), force sensors, and surface electromyography (EMG) were identified as key technologies that can be used to aid health and safety professionals, ergonomists, and human factors practitioners improve user performance and monitor risk. IMU-based solutions were the most used wearable types in both sectors. Industry largely used biomechanical wearables to assess tasks and risks wholistically, which sports often considered the individual components of movement and performance. Availability, cost, and adoption remain common limitation issues across both sports and industrial applications.

Clinician Impact on Athlete Recovery and Readiness in a 24-Hour Training Cycle

This paper explores a 24-hr training cycle and how clinicians contribute to an athlete’s transition from recovery to readiness. The cycle is divided into three phases: immediate, intermediate, and extended. Phase break down is meant to provide wellness prioritization for the athlete and how the clinician can facilitate sustainable performance during a competitive season.

Design of a Capacitance-to-Digital Converter Based on Iterative Delay-Chain Discharge in 180 nm CMOS Technology

The design of advanced miniaturized ultra-low power interfaces for sensors is extremely important for energy-constrained monitoring applications, such as wearable, ingestible and implantable devices used in the health and medical field. Capacitive sensors, together with their correspondent digital-output readout interfaces, make no exception. Here, we analyse and design a capacitance-to-digital converter, based on the recently introduced iterative delay-chain discharge architecture, showing the circuit inner operating principles and the correspondent design trade-offs. A complete design case, implemented in a commercial 180 nm CMOS process, operating at 0.9 V supply for a 0-250 pF input capacitance range, is presented. The circuit, tested by means of detailed electrical simulations, shows ultra-low energy consumption (≤1.884 nJ/conversion), excellent linearity (linearity error 15.26 ppm), good robustness against process and temperature corners (conversion gain sensitivity to process corners variation of 114.0 ppm and maximum temperature sensitivity of 81.9 ppm/°C in the -40 °C, +125 °C interval) and medium-low resolution of 10.3 effective number of bits, while using only 0.0192 mm2 of silicon area and employing 2.93 ms for a single conversion.

Short-Term Skin Temperature Responses to Endurance Exercise: A Systematic Review of Methods and Future Challenges in the Use of Infrared Thermography

BACKGROUND

Body temperature is often assessed in the core and the skin. Infrared thermography has been used to measure skin temperature (Tsk) in sport research and clinical practice. This study aimed to explore the information reported to date on the use of infrared thermography to detect short-term Tsk responses to endurance exercise and to identify the methodological considerations and knowledge gaps, and propose future directions.

METHOD

A web search (PubMed, Science Direct, Google Scholar, and Web of Science) was conducted following systematic review guidelines, and 45 out of 2921 studies met the inclusion criteria (endurance sports, since 2000, English, full text available).

RESULTS

A total of 45 publications were extracted, in which most of the sample were runners (n = 457, 57.9%). Several differences between IRT imaging protocols and ROI selection could lead to potential heterogeneity of interpretations. These particularities in the methodology of the studies extracted are widely discussed in this systematic review.

CONCLUSIONS

More analyses should be made considering different sports, exercise stimuli and intensities, especially using follow-up designs. Study-derived data could clarify the underlying thermo physiological processes and assess whether Tsk could be used a reliable proxy to describe live thermal regulation in endurance athletes and reduce their risk of exertional heat illness/stroke. Also more in-depth analyses may elucidate the Tsk interactions with other tissues during exercise-related responses, such as inflammation, damage, or pain.

On the usability of digital sleep interventions in sports

High sleep quality is highly related to better health and peak performance. Nowadays, multiple applications and platforms are available to track activity, to monitor heart rate, or to evaluate sleep quality. While activity tracking and heart rate monitoring are widely used, little is known about the potentials of digital tools to optimize sleep quality. Especially among athletes, who often suffer from reduced sleep quality because of full schedules and high competition performance pressure, interventions to maximize performance by optimizing recovery and sleep quality seem to be promising. In the present paper, we give an overview on existing research focusing on the potentials of digital interventions to enhance sleep quality among athletes. In particular, mindfulness-based digital interventions seem to be promising as they evidently foster high sleep quality and related health and performance patterns. Further, athletes can time- and cost-effectively integrate them into their daily routines. Future research is needed to empirically test the usability of digital features and suitable interventions to optimize sleep.

The Wearable Physical Fitness Training Device Based on Fuzzy Theory

Mobile Edge Computing and Communication (MECC) can be deployed in close proximity with sensing devices and act as middleware between cloud and local networks. The health and fitness movement has become extremely popular recently. Endurance activities, such as marathons, triathlons, and cycling have also grown in popularity. However, with more people participating in these activities, more accidents and injuries occur—ranging from heat stroke, to heart attacks, shock, or hypoxia. All physical training activities include a risk of injury and accidents. Therefore, any research that offers a means of reducing injury risk will significantly contribute to the personal fitness field. Moreover, with the growing popularity of wearable devices and the rise of the MECC, the development and application of wearable devices that can connect to the MECC has become widespread, producing many new innovations. Although many wearable devices, such as wrist straps and smart watches, are available and able to detect individual physiological data, they cannot monitor the human body in a state of motion. Therefore, this study proposes a set of monitoring parameters for a novel wearable device connected to the MECC based on fitness management to assist fitness trainers in effective prompted strength training, and to offer timely warnings in the event of an injury risk. The data collected by the monitoring device using fuzzy theory include risk factor, body temperature, heart rate, and blood oxygen concentration. The proposed system can display in real-time the current physiological state of a wearer/user. The introduction of this device will hopefully enable trainers to immediately and effectively control and monitor the intensity of a training session, while increasing training safety, and offer crucial and immediate diagnostic information so that the correct treatment can be applied without delay in the event of injury.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Accurate Blood Pressure Estimation During Activities of Daily Living: A Wearable Cuffless Solution

The objective is to develop a cuffless method that accurately estimates blood pressure (BP) during activities of daily living. User-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using artificial neural networks to estimate the BP waveforms from electrocardiography and photoplethysmography signals. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. The procedure was performed before and after a six-hour testing phase wherein five participants went about their normal daily living activities. Data were further collected at a four-month time point for two participants and again at six months for one of the two. The performance of three different NARX models was compared with three pulse arrival time (PAT) models. The NARX models demonstrate superior accuracy and correlation with "ground truth" systolic and diastolic BP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. This establishes a method for cuffless BP estimation during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection.

Exploring the Use of Mobile and Wearable Technology among University Student Athletes in Lebanon: A Cross-Sectional Study

The markets of commercial wearables and health and fitness apps are constantly growing globally, especially among young adults and athletes, to track physical activity, energy expenditure and health. Despite their wide availability, evidence on use comes predominantly from the United States or Global North, with none targeting college student-athletes in low- and middle-income countries. This study was aimed to explore the use of these technologies among student-athletes at the American University of Beirut (AUB). We conducted a cross-sectional survey of 482 participants (average age 20 years) enrolled in 24 teams during Fall 2018; 230 students successfully completed the web-based survey, and 200 provided valid data. Fifty-three (26.5%) have owned a fitness tracker, mostly for self-monitoring. The most popular were Fitbit, Apple Watch, and Garmin. Similarly, 82 students (40%) used apps, primarily MyFitnessPal, Apple Health, and Samsung Health. Nevertheless, many participants discontinued use due to loss of interest or technical issues (breaking, usability, obsolescence, or lack of engagement). Wearable devices were considered superior to mobile phones alone as physical activity monitors. However, forming regular habits made self-monitoring via technology irrelevant. Further research is needed to better understand what motivates continuous use among student-athletes, who could use trackers to improve athletic performance and overall health.

Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes

Dehydration beyond 2% bodyweight loss should be monitored to reduce the risk of heat-related injuries during exercise. However, assessments of hydration in athletic settings can be limited in their accuracy and accessibility. In this study, we sought to develop a data-driven noninvasive approach to measure hydration status, leveraging wearable sensors and normal orthostatic movements. Twenty participants (10 males, 25.0 ± 6.6 years; 10 females, 27.8 ± 4.3 years) completed two exercise sessions in a heated environment: one session was completed without fluid replacement. Before and after exercise, participants performed 12 postural movements that varied in length (up to 2 min). Logistic regression models were trained to estimate dehydration status given their heart rate responses to these postural movements. The area under the receiver operating characteristic curve (AUROC) was used to parameterize the model's discriminative ability. Models achieved an AUROC of 0.79 (IQR: 0.75, 0.91) when discriminating 2% bodyweight loss. The AUROC for the longer supine-to-stand postural movements and shorter toe-touches were similar (0.89, IQR: 0.89, 1.00). Shorter orthostatic tests achieved similar accuracy to clinical tests. The findings suggest that data from wearable sensors can be used to accurately estimate mild dehydration in athletes. In practice, this method may provide an additional measurement for early intervention of severe dehydration.

Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015-2019

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19-64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO_2max ). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R ²: 0.870, P < 0.001), muscular endurance (adjusted R ²: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R ²: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R ²: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages.

Clusters of Adolescent Physical Activity Tracker Patterns and Their Associations With Physical Activity Behaviors in Finland and Ireland: Cross-Sectional Study

Background

Physical activity trackers (PATs) such as apps and wearable devices (eg, sports watches, heart rate monitors) are increasingly being used by young adolescents. Despite the potential of PATs to help monitor and improve moderate-to-vigorous physical activity (MVPA) behaviors, there is a lack of research that confirms an association between PAT ownership or use and physical activity behaviors at the population level.

Objective

The purpose of this study was to examine the ownership and use of PATs in youth and their associations with physical activity behaviors, including daily MVPA, sports club membership, and active travel, in 2 nationally representative samples of young adolescent males and females in Finland and Ireland.

Methods

Comparable data were gathered in the 2018 Finnish School-aged Physical Activity (F-SPA 2018, n=3311) and the 2018 Irish Children’s Sport Participation and Physical Activity (CSPPA 2018, n=4797) studies. A cluster analysis was performed to obtain the patterns of PAT ownership and usage by adolescents (age, 11-15 years). Four similar clusters were identified across Finnish and Irish adolescents: (1) no PATs, (2) PAT owners, (3) app users, and (4) wearable device users. Adjusted binary logistic regression analyses were used to evaluate how PAT clusters were associated with physical activity behaviors, including daily MVPA, membership of sports clubs, and active travel, after stratification by gender.

Results

The proportion of app ownership among Finnish adolescents (2038/3311, 61.6%) was almost double that of their Irish counterparts (1738/4797, 36.2%). Despite these differences, the clustering patterns of PATs were similar between the 2 countries. App users were more likely to take part in daily MVPA (males, odds ratio [OR] 1.27, 95% CI 1.04-1.55; females, OR 1.49, 95% CI 1.20-1.85) and be members of sports clubs (males, OR 1.37, 95% CI 1.15-1.62; females, OR 1.25, 95% CI 1.07-1.50) compared to the no PATs cluster, after adjusting for country, age, family affluence, and disabilities. These associations, after the same adjustments, were even stronger for wearable device users to participate in daily MVPA (males, OR 1.83, 95% CI 1.49-2.23; females, OR 2.25, 95% CI 1.80-2.82) and be members of sports clubs (males, OR 1.88, 95% CI 1.55-2.88; females, OR 2.07, 95% CI 1.71-2.52). Significant associations were observed between male users of wearable devices and taking part in active travel behavior (OR 1.39, 95% CI 1.04-1.86).

Conclusions

Although Finnish adolescents report more ownership of PATs than Irish adolescents, the patterns of use and ownership remain similar among the cohorts. The findings of our study show that physical activity behaviors were positively associated with wearable device users and app users. These findings were similar between males and females. Given the cross-sectional nature of this data, the relationship between using apps or wearable devices and enhancing physical activity behaviors requires further investigation.

Multifractal Analysis of Movement Behavior in Association Football

Research in football has been embracing the complex systems paradigm in order to identify different insights about key determinants of performance. The present study explored the multifractal properties of several football-related scenarios, as a candidate method to describe movement dynamics. The sample consisted of five footballers that were engaged in six different training situations (jogging, high intensity interval protocol, running circuit, 5 vs. 5, 8 vs. 8 and a 10 vs. 10 small-sided game). All kinematic measures were collected using a 100 Hz wireless and wearable inertial measurement unit (WIMUPRO©). Data were processed using a discrete wavelet leader transform in order to obtain a spectrum of singularities that could best describe the movement dynamics. The Holder exponent for each of all six conditions revealed mean values h < 0.5 indicating presence of long memory with anti-correlated behavior. A strong trend was found between the width of the multifractal spectrum and the type of task performed, with jogging showing the weakest multifractality ∆h = 0.215 ± 0.020, whereas, 10 vs. 10 small-sided game revealed the strongest ∆h = 0.992 ± 0.104. The Hausdorff dimension indicates that a maximal fluctuation rate occurs with a higher probability than that of the minimal fluctuation rate for all tasks, with the exception of the high intensity interval protocol. Moreover, the spectrum asymmetry values of jogging, running circuit, 5 vs. 5, 8 vs. 8 and 10 vs. 10 small-sided games reveal their multifractal structures are more sensitive to the local fluctuations with small magnitudes. The multifractal analysis has shown a potential to systematically elucidate the dynamics and variability structure over time for the training situations.

Survey on virtual coaching for older adults

Virtual coaching has emerged as a promising solution to extend independent living for older adults. A virtual coach system is an always-attentive personalized system that continuously monitors user's activity and surroundings and delivers interventions - that is, intentional messages - in the appropriate moment. This article presents a survey of different approaches in virtual coaching for older adults, from the less technically supported tools to the latest developments and future avenues for research. It focuses on the technical aspects, especially on software architectures, user interaction and coaching personalization. Nevertheless, some aspects from the fields of personality/social psychology are also presented in the context of coaching strategies. Coaching is considered holistically, including matters such as physical and cognitive training, nutrition, social interaction and mood.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments

The COVID-19 pandemic has brought into sharp focus the need to harness and leverage our digital infrastructure for remote patient monitoring. As current viral tests and vaccines are slow to emerge, we see a need for more robust disease detection and monitoring of individual and population health, which could be aided by wearable sensors. While the utility of this technology has been used to correlate physiological metrics to daily living and human performance, the translation of such technology toward predicting the incidence of COVID-19 remains a necessity. When used in conjunction with predictive platforms, users of wearable devices could be alerted when changes in their metrics match those associated with COVID-19. Anonymous data localized to regions such as neighborhoods or zip codes could provide public health officials and researchers a valuable tool to track and mitigate the spread of the virus, particularly during a second wave. Identifiable data, for example remote monitoring of cohorts (family, businesses, and facilities) associated with individuals diagnosed with COVID-19, can provide valuable data such as acceleration of transmission and symptom onset. This manuscript describes clinically relevant physiological metrics which can be measured from commercial devices today and highlights their role in tracking the health, stability, and recovery of COVID-19+ individuals and front-line workers. Our goal disseminating from this paper is to initiate a call to action among front-line workers and engineers toward developing digital health platforms for monitoring and managing this pandemic.

Smart Shirts for Monitoring Physiological Parameters: Scoping Review

BACKGROUND

The recent trends of technological innovation and widescale digitization as potential solutions to challenges in health care, sports, and emergency service operations have led to the conception of smart textile technology. In health care, these smart textile systems present the potential to aid preventative medicine and early diagnosis through continuous, noninvasive tracking of physical and mental health while promoting proactive involvement of patients in their medical management. In areas such as sports and emergency response, the potential to provide comprehensive and simultaneous physiological insights across multiple body systems is promising. However, it is currently unclear what type of evidence exists surrounding the use of smart textiles for the monitoring of physiological outcome measures across different settings.

OBJECTIVE

This scoping review aimed to systematically survey the existing body of scientific literature surrounding smart textiles in their most prevalent form, the smart shirt, for monitoring physiological outcome measures.

METHODS

A total of 5 electronic bibliographic databases were systematically searched (Ovid Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Scopus, Cumulative Index to Nursing and Allied Health Literature, and SPORTDiscus). Publications from the inception of the database to June 24, 2019 were reviewed. Nonindexed literature relevant to this review was also systematically searched. The results were then collated, summarized, and reported.

RESULTS

Following the removal of duplicates, 7871 citations were identified. On the basis of title and abstract screening, 7632 citations were excluded, whereas 239 were retrieved and assessed for eligibility. Of these, 101 citations were included in the final analysis. Included studies were categorized into four themes: (1) prototype design, (2) validation, (3) observational, and (4) reviews. Among the 101 analyzed studies, prototype design was the most prevalent theme (50/101, 49.5%), followed by validation (29/101, 28.7%), observational studies (21/101, 20.8%), and reviews (1/101, 0.1%). Presented prototype designs ranged from those capable of monitoring one physiological metric to those capable of monitoring several simultaneously. In 29 validation studies, 16 distinct smart shirts were validated against reference technology under various conditions and work rates, including rest, submaximal exercise, and maximal exercise. The identified observational studies used smart shirts in clinical, healthy, and occupational populations for aims such as early diagnosis and stress detection. One scoping review was identified, investigating the use of smart shirts for electrocardiograph signal monitoring in cardiac patients.

CONCLUSIONS

Although smart shirts have been found to be valid and reliable in the monitoring of specific physiological metrics, results were variable for others, demonstrating the need for further systematic validation. Analysis of the results has also demonstrated gaps in knowledge, such as a considerable lag of validation and observational studies in comparison with prototype design and limited investigation using smart shirts in pediatric, elite sports, and emergency service populations.

Potentials of Digitalization in Sports Medicine: A Narrative Review

Digital transformation is becoming increasingly common in modern life and sports medicine, like many other medical disciplines, it is strongly influenced and impacted by this rapidly changing field. This review aims to give a brief overview of the potential that digital technologies can have for health care providers and patients in the clinical practice of sports medicine. We will focus on mobile applications, wearables, smart devices, intelligent machines, telemedicine, artificial intelligence, big data, system interoperability, virtual reality, augmented reality, exergaming, or social networks. While some technologies are already used in current medical practice, others still have undiscovered potential. Due to the diversity and ever changing nature of this field, we will briefly review multiple areas in an attempt to give readers some general exposure to the landscape instead of a thorough, deep review of one topic. Further research will be necessary to show how digitalization applications could best be used for patient treatments.

Landing Biomechanics, But Not Physical Activity, Differ in Young Male Athletes With and Without Patellar Tendinopathy

OBJECTIVE

To examine differences in biomechanical and physical activity load in young male athletes with and without patellar tendinopathy.

DESIGN

Cross-sectional cohort study.

METHODS

Forty-one young male athletes (15-28 years of age) were categorized into 3 distinct groups: symptomatic athletes with patellar tendon abnormalities (PTA) (n = 13), asymptomatic athletes with PTA (n = 14), and a control group of asymptomatic athletes without PTA (n = 14). Participants underwent a laboratory biomechanical jump-landing assessment and wore an accelerometer for 1 week of physical activity monitoring.

RESULTS

The symptomatic group demonstrated significantly less patellar tendon force loading impulse in the involved limb compared with both the control and asymptomatic groups (P<.05), with large effects (d = 0.91-1.40). There were no differences in physical activity between the 3 groups (P>.05).

CONCLUSION

Young male athletes with symptomatic patellar tendinopathy demonstrated smaller magnitudes of patellar tendon force loading impulse during landing compared to both asymptomatic athletes with patellar tendinopathy and healthy control participants. However, these 3 distinct groups did not differ in general measures of physical activity. Future investigations should examine whether comprehensively monitoring various loading metrics may be valuable to avoid both underloading and overloading patterns in athletes with patellar tendinopathy. J Orthop Sports Phys Ther 2020;50(3):158-166. Epub 6 Jan 2020. doi:10.2519/jospt.2020.9065.

Does Overexertion Correlate With Increased Injury? The Relationship Between Player Workload and Soft Tissue Injury in Professional American Football Players Using Wearable Technology

BACKGROUND

The relationship of training load to injury using wearable technology has not been investigated in professional American football players. The primary objective of this study was to determine the correlation between player workload and soft tissue injury over the course of a football season utilizing wearable global positioning system (GPS) technology.

HYPOTHESIS

Increased training load is associated with a higher incidence of soft tissue injuries.

STUDY DESIGN

Case-control study.

LEVEL OF EVIDENCE

Level 3.

METHODS

Player workloads were assessed during preseason and regular-season practice sessions using GPS tracking and triaxial accelerometry from 2014 to 2016. Soft tissue injuries were recorded during each season. Player workload during the week of injury (acute) and average weekly workload during the 4 weeks (chronic) prior to injury were determined for each injury and in uninjured position-matched controls during the same week. A matched-pairs t test was used to determine differences in player workload. Subgroup analysis was also conducted to determine whether observed effects were confounded by training period and type of injury.

RESULTS

In total, 136 lower extremity injuries were recorded. Of the recorded injuries, 101 injuries with complete GPS and clinical data were included in the analysis. Injuries were associated with greater increases in workload during the week of injury over the prior month when compared with uninjured controls. Injured players saw a 111% (95% CI, 66%-156%) increase in workload whereas uninjured players saw a 73% (95% CI, 34%-112%) increase in workload during the week of injury (P = 0.032). Individuals who had an acute to chronic workload ratio higher than 1.6 were 1.5 times more likely to sustain an injury relative to time- and position-matched controls (64.6% vs 43.1%; P = 0.004).

CONCLUSION

Soft tissue injuries in professional football players were associated with sudden increases in training load over the course of a month. This effect seems to be especially pronounced during the preseason when player workloads are generally higher. These results suggest that a gradual increase of training intensity is a potential method to reduce the risk of soft tissue injury.

CLINICAL RELEVANCE

Preseason versus regular-season specific training programs monitored with wearable technology may assist team athletic training and medical staff in developing programs to optimize player performance.

Wearable sensors for monitoring the internal and external workload of the athlete

The convergence of semiconductor technology, physiology, and predictive health analytics from wearable devices has advanced its clinical and translational utility for sports. The detection and subsequent application of metrics pertinent to and indicative of the physical performance, physiological status, biochemical composition, and mental alertness of the athlete has been shown to reduce the risk of injuries and improve performance and has enabled the development of athlete-centered protocols and treatment plans by team physicians and trainers. Our discussions in this review include commercially available devices, as well as those described in scientific literature to provide an understanding of wearable sensors for sports medicine. The primary objective of this paper is to provide a comprehensive review of the applications of wearable technology for assessing the biomechanical and physiological parameters of the athlete. A secondary objective of this paper is to identify collaborative research opportunities among academic research groups, sports medicine health clinics, and sports team performance programs to further the utility of this technology to assist in the return-to-play for athletes across various sporting domains. A companion paper discusses the use of wearables to monitor the biochemical profile and mental acuity of the athlete.

Clinical translation of biomedical sensors for sports medicine

The digital health field has seen a surge in product development over the last decade, with product introductions ranging from wrist monitors, epidermal electronics, electronic pills and smart garments, much of these precipitated through the commercialisation and commoditisation of sensor technology. The emergence of wearable technology has recently garnered heightened interest by physicians and the general public. The convenient use of wireless technology to track and monitor physiological parameters, such as heart rate, distance, sleep and stress, has emerged to become relevant to patient care and human performance assessment. However, collecting data is not enough to inform clinical decision-making. It is essential to translate the acquired data into information relevant to clinicians. Our experiences tell us that team competencies must mirror the interdisciplinary technology itself. Thus, an interdisciplinary team blending expertise from engineering, medicine, and nursing is believed to be essential in translating wearable technology into the field. This review discusses the application of wearable sensors to monitor human performance assessment in domains necessitating accurate, reliable, and timely transmission of acquired bio-metric and bio-vital data. A key result disseminating from our investigations is the need to develop predictive models based off of the data acquired from wearable devices to necessitate the development of athlete-centred treatment plans to expedite the return-to-play time and to maximise performance.

CMOS Interfaces for Internet-of-Wearables Electrochemical Sensors: Trends and Challenges

Smart wearables, among immediate future IoT devices, are creating a huge and fast growing market that will encompass all of the next decade by merging the user with the Cloud in a easy and natural way. Biological fluids, such as sweat, tears, saliva and urine offer the possibility to access molecular-level dynamics of the body in a non-invasive way and in real time, disclosing a wide range of applications: from sports tracking to military enhancement, from healthcare to safety at work, from body hacking to augmented social interactions. The term Internet of Wearables (IoW) is coined here to describe IoT devices composed by flexible smart transducers conformed around the human body and able to communicate wirelessly. In addition the biochemical transducer, an IoW-ready sensor must include a paired electronic interface, which should implement specific stimulation/acquisition cycles while being extremely compact and drain power in the microwatts range. Development of an effective readout interface is a key element for the success of an IoW device and application. This review focuses on the latest efforts in the field of Complementary Metal–Oxide–Semiconductor (CMOS) interfaces for electrochemical sensors, and analyses them under the light of the challenges of the IoW: cost, portability, integrability and connectivity.

Wearables, Biomechanical Feedback, and Human Motor-Skills’ Learning & Optimization

Biomechanical feedback is a relevant key to improving sports and arts performance. Yet, the bibliometric keyword analysis on Web of Science publications reveals that, when comparing to other biofeedback applications, the real-time biomechanical feedback application lags far behind in sports and arts practice. While real-time physiological and biochemical biofeedback have seen routine applications, the use of real-time biomechanical feedback in motor learning and training is still rare. On that account, the paper aims to extract the specific research areas, such as three-dimensional (3D) motion capture, anthropometry, biomechanical modeling, sensing technology, and artificial intelligent (AI)/deep learning, which could contribute to the development of the real-time biomechanical feedback system. The review summarizes the past and current state of biomechanical feedback studies in sports and arts performance; and, by integrating the results of the studies with the contemporary wearable technology, proposes a two-chain body model monitoring using six IMUs (inertial measurement unit) with deep learning technology. The framework can serve as a basis for a breakthrough in the development. The review indicates that the vital step in the development is to establish a massive data, which could be obtained by using the synchronized measurement of 3D motion capture and IMUs, and that should cover diverse sports and arts skills. As such, wearables powered by deep learning models trained by the massive and diverse datasets can supply a feasible, reliable, and practical biomechanical feedback for athletic and artistic training.

Energy Cost of Continuous Shuttle Running: Comparison of 4 Measurement Methods

Ciprandi, D, Lovecchio, N, Piacenza, M, Limonta, E, Esposito, F, Sforza, C, Zago, M. Energy cost of continuous shuttle running: Comparison of 4 measurement methods. J Strength Cond Res 32(8): 2265-2272, 2018-Assessing runs with frequent turns (shuttle run) is a viable option to evaluate the energy cost associated with sport-specific high-intensity intermittent activities. To date, no study investigated the extent to which the computation of energy cost of exercise is affected by the following factors: procedure and duration of oxygen uptake measurement during exercise, oxygen uptake measurement during recovery, estimation of the anaerobic alactic contribution, consideration of respiratory exchange ratio (RER) in the computation, and exercise intensity. Therefore, the aim of the current study was to determine whether these factors may lead to different estimations of the energy cost of locomotion. Twenty-six healthy young men participated in two 5-m shuttle-run trials at an average speed of 50 and 75% of their maximal aerobic velocity, respectively. Oxygen uptake and lactate concentration were measured before, during, and after the trials. Results revealed that different methods of computing the energy cost of 5-m shuttle run returned significantly different results, in particular at high intensity levels. The largest significant difference found between methods was lower than 10%. This suggests that for the most accurate computation of the workload, the contribution of the anaerobic alactic mechanisms and the influence of the RER cannot be neglected. These findings might help sport scientists and conditioning trainers in identifying the exercise conditions in which including all the metabolic components are required for an accurate computation of athletes' energy expenditure. In turn, exercise conditions would be defined where the computation could be conveniently simplified without worsening results reliability.

Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.