Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

CaloriNet: From silhouettes to calorie estimation in private environments

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.Comment: 11 pages, 7 figure

An evaluation of energy expenditure estimation by three activity monitors

This is an Author's Accepted Manuscript of an article published in European Journal of Sport Science, 13(6), 681 - 688, 2013 [date of publication] [copyright Taylor & Francis], available online at: http://www.tandfonline.com/ 10.1080/17461391.2013.776639.A comparative evaluation of the ability of activity monitors to predict energy expenditure (EE) is necessary to aid in the investigation of the effect of EE on health. The purpose of this study was to validate and compare the RT3, the SWA and the IDEEA at measuring EE in adults and children. Twenty-six adults and 22 children completed a resting metabolic rate (RMR) test and performed four treadmill activities at 3 km.h−1, 6 km.h−1, 6 km.h−1 at a 10% incline, 9 km.h−1. EE was assessed throughout the protocol by the RT3, the SWA and the IDEEA. Indirect calorimetry (IC) was used as a criterion measure of EE against which each monitor was compared. Mean bias was assessed by subtracting EE from IC from EE from each monitor for each activity. Limit of agreement plots were used to assess the agreement between each monitor and IC. Limits of agreement for resting EE were narrowest for the RT3 for adults and children. Although the IDEEA displayed the smallest mean bias between measures at 3 km.h−1, 6 km.h−1 and 9 km.h−1 in adults and children, the SWA agreed closest with IC at 6 km.h−1, 6 km.h−1 at a 10% incline and 9 km.h−1. Limits of agreement were closest for the SWA at 9 km.h−1 in adults representing 42% of the overall mean EE. Although the RT3 provided the best estimate of resting EE in adults and children, the SWA provided the most accurate estimate of EE across a range of physical activity intensities

Physical activity assessment under free-living conditions using pattern-recognition monitors

Extensive literature has documented the health benefits of physical activity. Valid, reliable and feasible physical activity assessment tools are necessary to assess the complexity and multidimensionality of physical activity behavior. Pattern-recognition activity monitors that integrate information from multiple sensors appear to be the most promising approach for assessing physical activity under free-living conditions. Previous studies have provided support to the validity of pattern-recognition monitors for assessing the energy cost of activity under-free living conditions in young adults. However, children and older adults present unique measurement challenges for the assessment of physical activity under free-living conditions. The series of studies in this dissertation extends previous research by assessing the accuracy of a pattern-recognition monitor (SenseWear Armband) in children and older adults under free-living conditions. Consistent with previous findings in young adults, results indicate that the SenseWear Armband monitors provide valid estimates of total energy expenditure and activity energy expenditure in older adults and children, under free-living conditions. Collectively, the findings of this research support the validity of the SenseWear Armband for assessing physical activity under free-living conditions in children and older adults

Incorporating Excess Post-exercise Oxygen Consumption into Accelerometer Energy Expenditure Estimation Algorithms

Accelerometers are objective monitors of physical activity (PA) that can be used to estimate energy expenditure (EE). Most accelerometer EE estimation equations are based on steady-state data and do not consider excess post-exercise oxygen consumption (EPOC) after exercise. PURPOSE: To quantify the error in accelerometer EE estimates due to EPOC after varying durations of high-intensity treadmill running. METHODS: Nine young, healthy, recreationally active males participated in three study visits. Visit 1 included a treadmill VO2 peak test to determine the treadmill speed correlating to 80% VO2 peak for visits 2 and 3. Visit 2 included a seated 20-min baseline and three short (30s, 60s, 120s) vigorous treadmill running bouts each followed by 20 minutes of seated rest. Visit 3 included a supine 60-min baseline and a 30-min treadmill running bout followed by 3 hours of supine rest. Twelve EE estimation equations each using either a non-dominant wrist or right hip ActiGraph GT3X+ accelerometer were compared to the true EE measured by the Parvomedics TrueOne 2400 indirect calorimeter. RESULTS: The Freedson 1998 EE estimation equation overestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: 19.3 [11.4, 27.2], 60s: 16.6 [8.5, 24.7], 120s: 13.4 [5.74, 21.1], 30min: 15.1 [6.69, 23.5]). The Crouter 2009 branching algorithm underestimated EE during the 20min post-exercise period after each exercise bout (mean kCals [95% CIs]; 30s: -8.59 [-10.6, -6.62], 60s: -11.6 [-13.7, -9.38], 120s: -15.0 [-18.1, -11.8], 30min: -11.0 [-14.3, -7.77]), but was partially corrected by adding in the measured EPOC. CONCLUSION: Estimated EE during lying or seated rest from linear accelerometer equations was heavily dependent on the y-intercept of the equation, which represents the estimated resting EE of the wearer, with the Crouter calibration study being the only one to directly measure resting EE. More sophisticated approaches, like the Crouter 2009 and newer machine learning algorithms, have better potential to more accurately estimate EE across various activity types. New accelerometer EE estimations should include resting in their calibration protocols in order to more accurately estimate EE during rest

Energy expenditure prediction via a footwear-based physical activity monitor: accuracy and comparison to other devices

2011 Summer.Includes bibliographical references.Accurately estimating free-living energy expenditure (EE) is important for monitoring or altering energy balance and quantifying levels of physical activity. The use of accelerometers to monitor physical activity and estimate physical activity EE is common in both research and consumer settings. Recent advances in physical activity monitors include the ability to identify specific activities (e.g. stand vs. walk) which has resulted in improved EE estimation accuracy. Recently, a multi]sensor footwear-based physical activity monitor that is capable of achieving 98% activity identification accuracy has been developed. However, no study has compared the EE estimation accuracy for this monitor and compared this accuracy to other similar devices. PURPOSE: To determine the accuracy of physical activity EE estimation of a footwear-based physical activity monitor that uses an embedded accelerometer and insole pressure sensors and to compare this accuracy against a variety of research and consumer physical activity monitors. METHODS: Nineteen adults (10 male, 9 female), mass: 75.14 (17.1) kg, BMI: 25.07(4.6) kg/m2 (mean (SD)), completed a four hour stay in a room calorimeter. Participants wore a footwear-based physical activity monitor, as well as three physical activity monitoring devices used in research: hip]mounted Actical and Actigraph accelerometers and a multi-accelerometer IDEEA device with sensors secured to the limb and chest. In addition, participants wore two consumer devices: Philips DirectLife and Fitbit. Each individual performed a series of randomly assigned and ordered postures/activities including lying, sitting (quietly and using a computer), standing, walking, stepping, cycling, sweeping, as well as a period of self-selected activities. We developed branched (i.e. activity specific) linear regression models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: The shoe-based device was not significantly different than the mean measured EE (476(20) vs. 478(18) kcal) (Mean(SE)), respectively, and had the lowest root mean square error (RMSE) by two]fold (29.6 kcal (6.19%)). The IDEEA (445(23) kcal) and DirecLlife (449(13) kcal) estimates of EE were also not different than the measured EE. The Actigraph, Fitbit and Actical devices significantly underestimated EE (339 (19) kcal, 363(18) kcal and 383(17) kcal, respectively (p<.05)). Root mean square errors were 62.1 kcal (14%), 88.2 kcal(18%), 122.2 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for DirectLife, IDEEA, Actigraph, Actical and Fitbit respectively. CONCLUSIONS: The shoe based physical activity monitor was able to accurately estimate EE. The research and consumer physical activity monitors tested have a wide range of accuracy when estimating EE. Given the similar hardware of these devices, these results suggest that the algorithms used to estimate EE are primarily responsible for their accuracy, particularly the ability of the shoe-based device to estimate EE based on activity classifications

Validity of energy expenditure estimation methods during 10 days of military training

Wearable physical activity (PA) monitors have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established research methods has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors and a PA log against doubly-labelled water (DLW) during British Army Officer Cadet (OC) training. For 10 days of training, twenty (10 male and 10 female) OCs (mean ± SD: age 23 ± 2 years, height 1.74 ± 0.09 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK) on the dominant wrist, wore one commercially-available monitor (Fitbit SURGE, USA) on the non-dominant wrist and completed a self-report PA log. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analysed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement (LoA) were employed to compare TEE from each estimation method to DLW. Average daily TEE from DLW was 4112 ± 652 kcal·day against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal day ) while Fitbit tended to underestimate (-656 ± 683 kcal·day ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day ). Wearable physical activity monitors provide a cheaper and more practical method for estimating free-living TEE than DLW in military settings. The GENEActiv accelerometer demonstrated good validity for assessing daily TEE and would appear suitable for use in large-scale, longitudinal military studies

The Estimation of Caloric Expenditure Using Three Triaxial Accelerometers

Accelerometer-based activity monitors are commonly used to measure physical activity energy expenditure (PAEE). Newly designed wrist and hip-worn triaxial accelerometers claim to accurately predict PAEE across a range of activities. Purpose: To determine if the Nike FuelBand (NFB), Fitbit (FB) and ActiGraph GT3X+ (AG) estimate PAEE in various activities. Methods: 21 healthy, college-aged adults wore a NFB on the right wrist, a FB on the left hip, and AG on the right hip, while performing 17 activities. AG data were analyzed using Freedson’s kcal regression equation. PAEE was measured using the Cosmed K4b2 (K4). Repeated measures ANOVAs were used to compare mean differences in PAEE (kcal/min). Paired sample t-tests with Bonferroni adjustments were used to locate significant differences. Results: For each device, the mean difference in PAEE was significantly different from the K4 (NFB, -0.45 + 2.8, FB, 0.48 + 2.27, AG, 0.64 + 2.59 kcal/min, p = 0.01). The NFB significantly overestimated most walking activities (e.g., regular walking; K4, 3.1 + 0.2 vs. NFB, 4.6 + 0.2 kcal/min) and activities with arm movements (e.g., sweeping; K4, 3.0 + 0.8 vs. NFB, 4.7 + 0.4 kcal/min, p \u3c 0.05). The NFB trended towards overestimating sport activities (basketball; K4, 10.8 + 0.8 vs. NFB, 12.2 + 0.5 kcal/min) (racquetball; K4, 9.6 + 0.8 vs. NFB 11.1 + 0.5 kcal/min). The FB and the AG significantly overestimated walking (K4, 3.1 + 0.2; FB, 5.4 + 0.3, AG, 5.8 + 0.4 kcal/min, p = 0.01) and underestimated PAEE of most activities with arm movements (e.g., Air Dyne, K4 5.6 + 0.2; Fitbit, 0.3 + 0.2; AG, 0.2 + 0.1 kcal/min, p \u3c 0.05) (racquetball, K4, 9.6 + 0.8 kcal/minute vs. FB, 7.4 + 0.6 kcal/minute, vs. AG, 6.5 + 0.4 kcal/minute, p \u3c 0.05). Conclusion: The NFB overestimated PAEE during most activities with arm movements and tended to overestimate sport activities, while the AG and FB overestimated walking and underestimated activities with arm movements. Overall, the wrist-worn NFB had similar accuracy to the waist-worn triaxial accelerometers; however, none of the devices were able to estimate PAEE across a range of activities