Physical activity characterization:Does one site fit all?

Background: It is evident that a growing number of studies advocate a wrist-worn accelerometer for the assessment of patterns of physical activity a priori, yet the veracity of this site rather than any other body-mounted location for its accuracy in classifying activity is hitherto unexplored. Objective: The objective of this review was to identify the relative accuracy with which physical activities can be classified according to accelerometer site and analytical technique. Methods: A search of electronic databases was conducted using Web of Science, PubMed and Google Scholar. This review included studies written in the English language, published between database inception and December 2017, which characterized physical activities using a single accelerometer and reported the accuracy of the technique. Results: A total of 118 articles were initially retrieved. After duplicates were removed and the remaining articles screened, 32 full-text articles were reviewed, resulting in the inclusion of 19 articles that met the eligibility criteria. Conclusion: There is no 'one site fits all' approach to the selection of accelerometer site location or analytical technique. Research design and focus should always inform the most suitable location of attachment, and should be driven by the type of activity being characterized

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data was continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals

Classification Accuracy of the Wrist-Worn GENEA Accelerometer During Structured Activity Bouts: A Cross-Validation Study

Purpose: The purpose of this study is to determine whether the left wrist cutpoints of Esliger et al., for the triaxial GENEA accelerometer, are accurate for predicting intensity categories during 14 different activities including; treadmill-based, home and office, and sport activities. Methods: 130 adults wore a GENEA accelerometer on their left wrist while performing various lifestyle activities. The Oxycon Mobile Portable Metabolic Unit was used to measure oxygen uptake during each activity. Statistical analysis used Spearman’s rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulation tables were constructed to describe under or over estimation of misclassified activities, and one-way chi-squares were used to test whether the accuracy rate of each activity differed from 80%. Results: For all activities the GENEA accelerometer-based physical activity monitor explained 41.1% of the energy expenditure. The GENEA correctly classified 52.8% of observations when all activities were combined. Five of the 14 activities showed no statistical difference in physical activity intensity classification estimation when compared to 80% accuracy, with 1 activity (treadmill jogging) showing statistically greater accuracy than 80%. For the remainder of the activities, the GENEA showed less than 80% accuracy for predicting intensity. Conclusion: Cross-validation of the proposed GENEA left wrist cutpoints classified the majority of activities performed significantly below the accuracy rate of 80%. Researchers should be cautious when applying the Esliger et al. cutpoints to a different population and activities not tested by those investigators

A 'one-size-fits-most' walking recognition method for smartphones, smartwatches, and wearable accelerometers

The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using 'activity counts,' a measure which overlooks specific types of physical activities. We proposed a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validated our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrated that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assessed the method's algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we have released our method as open-source software in MATLAB and Python.Comment: 39 pages, 4 figures (incl. 1 supplementary), and 5 tables (incl. 2 supplementary

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

Evaluation of an open source method for calculating physical activity in dogs from harness and collar based sensors

Abstract Background The ability to make objective measurements of physical activity in dogs has both clinical and research applications. Accelerometers offer a non-intrusive and convenient solution. Of the commercialy available sensors, measurements are commonly given in manufacturer bespoke units and calculated with closed source approaches. Furthermore, the validation studies that exist for such devices are mounting location dependant, not transferable between brands or not suitable for handling modern raw accelerometry type data. Methods This paper describes a validation study of n = 5 where 4 sensors were placed on each dog; 2 on a harness and 2 on a collar. Each position held two sensors from different manufacturers; Actigraph (which has previously been validated for use on the collar) and VetSens (which provides un-filtered accelerometry data). The aims of the study was to firstly evaluate the performance of an open-design method of converting raw accelerometry data into units that have previously been validated. Secondly, comparison was made between sensors mounted at each location for determining physical activity state. Results Once the raw actigraphy data had been processed with the open-design method, results from a 7 day measurement revealed no significant difference in physical activity estimates via a cutpoint approach between the sensor manufacturers. A second finding was a low inter-site variability between the ventral collar and dorsal harness locations (Pearsons r2 = 0.96). Conclusions Using the open-design methodology, raw, un-filtered data from the VetSens sensors can be compared or pooled with data gathered from Actigraph sensors. The results also provide strong evidence that ventral collar and dorsal harness sites may be used interchangeably. This enables studies to be designed with a larger inclusion criteria (encompassing dogs that are not well suited for wearing an instrumented collar) and ensures high levels of welfare while maintaining measurement validity

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future