Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting

Five-day evaluation of the acceptability and comfort of wearable technology at four anatomical locations during military training

INTRODUCTION

Wearable sensor technologies enable Defence to optimise human performance, remotely identify physiological abnormalities and enhance medical support. Maximising the acceptability of devices will ensure they are worn alongside other equipment. This study assessed the acceptability and comfort of four devices at different anatomical locations during military training.

METHOD

A cross-sectional pilot study during a live firing infantry exercise or adventurous training assessed four anatomical locations concurrently over 5 days: finger, wrist, upper arm and chest. Participants rated comfort, acceptability and preference using a standardised questionnaire after 12 hours and 5 days of wear.

RESULTS

Twenty-one regular British Army personnel soldiers participated, aged 24.4 (4.3) years. The upper arm location received the highest rating by participants for comfort, followed in order by wrist, finger and chest (p=0.002, Χ2=40.0). The finger was most commonly identified as uncomfortable during specific activities (76%), followed by chest (48%), wrist (23%) and upper arm devices (14%). There was no significant difference in participant confidence in the devices to collect data or allow movement, but there was a trend towards greater confidence in upper arm and wrist locations to stay in position than the others (p=0.059, Χ2=28.0). After 5 days of wear, 43% of participants said they preferred the upper arm for comfort, followed by wrist (36%), finger (24%) and chest (10%). 73% and 71% would wear the wrist and upper arm devices on deployed operations, compared with 29% and 24% for chest and finger devices, respectively.

CONCLUSION

The upper arm location offered greater acceptability and comfort than finger, wrist or chest locations. It is essential to consider such findings from occupationally relevant settings when selecting wearable technology. A larger service evaluation in diverse settings is recommended to guide the choice of the most acceptable wearable devices across different equipment, roles and environments.

With life there is motion. Activity biomarkers signal important health and performance outcomes

Measures of human motion provide a rich source of health and physiological status information. This paper provides examples of motion-based biomarkers in the form of patterns of movement, quantified physical activity, and characteristic gaits that can now be assessed with practical measurement technologies and rapidly evolving physiological models and algorithms, with research advances fed by the increasing access to motion data and associated contextual information. Quantification of physical activity has progressed from step counts to good estimates of energy expenditure, useful to weight management and to activity-based health outcomes. Activity types and intensity durations are important to health outcomes and can be accurately classified even from carried smart phone data. Specific gaits may predict injury risk, including some re-trainable injurious running or modifiable load carriage gaits. Mood status is reflected in specific types of human movement, with slumped posture and shuffling gait signaling depression. Increased variability in body sway combined with contextual information may signify heat strain, physical fatigue associated with heavy load carriage, or specific neuropsychological conditions. Movement disorders might be identified earlier and chronic diseases such as Parkinson's can be better medically managed with automatically quantified information from wearable systems. Increased path tortuosity suggests head injury and dementia. Rapidly emerging wear-and-forget systems involving global positioning system and inertial navigation, triaxial accelerometry, smart shoes, and functional fiber-based clothing are making it easier to make important health and performance outcome associations, and further refine predictive models and algorithms that will improve quality of life, protect health, and enhance performance.

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.

A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices

We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.

An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity

Sportswear-type wearables with integrated inertial sensors and electrocardiogram (ECG) electrodes have been commercially developed. We evaluated the feasibility of using a sportswear-type wearable with integrated inertial sensors and electrocardiogram (ECG) electrodes for evaluating exercise intensity within a controlled laboratory setting. Six male college athletes were asked to wear a sportswear-type wearable while performing a treadmill test that reached up to 20 km/h. The magnitude of the filtered tri-axial acceleration signal, recorded by the inertial sensor, was used to calculate the acceleration index. The R-R intervals of the ECG were used to determine heart rate; the external validity of the heart rate was then evaluated according to oxygen uptake, which is the gold standard for physiological exercise intensity. Single regression analysis between treadmill speed and the acceleration index in each participant showed that the slope of the regression line was significantly greater than zero with a high coefficient of determination (walking, 0.95; jogging, 0.96; running, 0.90). Another single regression analysis between heart rate and oxygen uptake showed that the slope of the regression line was significantly greater than zero, with a high coefficient of determination (0.96). Together, these results indicate that the sportswear-type wearable evaluated in this study is a feasible technology for evaluating physical and physiological exercise intensity across a wide range of physical activities and sport performances.