Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors

Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. Results: GENEA produced significantly higher (p \u3c 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p \u3c 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors. Conclusions: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration

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

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

Can smartwatches replace smartphones for posture tracking?

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed

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

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