scholarly journals Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study (Preprint)

2018 ◽  
Author(s):  
Anis Davoudi ◽  
Amal Asiri Wanigatunga ◽  
Matin Kheirkhahan ◽  
Duane Benjamin Corbett ◽  
Tonatiuh Mendoza ◽  
...  
Keyword(s):  
Physical Activity ◽  
Random Forest ◽  
Activity Recognition ◽  
Body Movement ◽  
Daily Activities ◽  
Accelerometer Data ◽  
Individual Activity ◽  
Activity Intensity ◽  
Location Detection ◽  
Mechanical Shaker

BACKGROUND Wearable accelerometers have greatly improved measurement of physical activity, and the increasing popularity of smartwatches with inherent acceleration data collection suggest their potential use in the physical activity research domain; however, their use needs to be validated. OBJECTIVE This study aimed to assess the validity of accelerometer data collected from a Samsung Gear S smartwatch (SGS) compared with an ActiGraph GT3X+ (GT3X+) activity monitor. The study aims were to (1) assess SGS validity using a mechanical shaker; (2) assess SGS validity using a treadmill running test; and (3) compare individual activity recognition, location of major body movement detection, activity intensity detection, locomotion recognition, and metabolic equivalent scores (METs) estimation between the SGS and GT3X+. METHODS To validate and compare the SGS accelerometer data with GT3X+ data, we collected data simultaneously from both devices during highly controlled, mechanically simulated, and less-controlled natural wear conditions. First, SGS and GT3X+ data were simultaneously collected from a mechanical shaker and an individual ambulating on a treadmill. Pearson correlation was calculated for mechanical shaker and treadmill experiments. Finally, SGS and GT3X+ data were simultaneously collected during 15 common daily activities performed by 40 participants (n=12 males, mean age 55.15 [SD 17.8] years). A total of 15 frequency- and time-domain features were extracted from SGS and GT3X+ data. We used these features for training machine learning models on 6 tasks: (1) individual activity recognition, (2) activity intensity detection, (3) locomotion recognition, (4) sedentary activity detection, (5) major body movement location detection, and (6) METs estimation. The classification models included random forest, support vector machines, neural networks, and decision trees. The results were compared between devices. We evaluated the effect of different feature extraction window lengths on model accuracy as defined by the percentage of correct classifications. In addition to these classification tasks, we also used the extracted features for METs estimation. RESULTS The results were compared between devices. Accelerometer data from SGS were highly correlated with the accelerometer data from GT3X+ for all 3 axes, with a correlation ≥.89 for both the shaker test and treadmill test and ≥.70 for all daily activities, except for computer work. Our results for the classification of activity intensity levels, locomotion, sedentary, major body movement location, and individual activity recognition showed overall accuracies of 0.87, 1.00, 0.98, 0.85, and 0.64, respectively. The results were not significantly different between the SGS and GT3X+. Random forest model was the best model for METs estimation (root mean squared error of .71 and r-squared value of .50). CONCLUSIONS Our results suggest that a commercial brand smartwatch can be used in lieu of validated research grade activity monitors for individual activity recognition, major body movement location detection, activity intensity detection, and locomotion detection tasks.

scholarly journals Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1214
Author(s):  
Eduardo Gomes ◽  
Luciano Bertini ◽  
Wagner Rangel Campos ◽  
Ana Paula Sobral ◽  
Izabela Mocaiber ◽  
...  
Keyword(s):  
Random Forest ◽  
Activity Recognition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Relevant Parameter ◽  
Accelerometer Data ◽  
K Nearest Neighbors ◽  
Activity Intensity ◽  
Potential Applications ◽  
Healthcare Monitoring

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

scholarly journals A Biomechanical Re-Examination of Physical Activity Measurement with Accelerometers

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3399 ◽  
Author(s):  
Jonatan Fridolfsson ◽  
Mats Börjesson ◽  
Daniel Arvidsson
Keyword(s):  
Physical Activity ◽  
Motion Capture ◽  
Group Differences ◽  
Band Pass Filter ◽  
Accelerometer Data ◽  
Age Group ◽  
Frequency Filtering ◽  
Frequency Filter ◽  
Step Frequency ◽  
Activity Intensity

ActiGraph is the most common accelerometer in physical activity research, but it has measurement errors due to restrictive frequency filtering. This study investigated biomechanically how different frequency filtering of accelerometer data affects assessment of activity intensity and age-group differences when measuring physical activity. Data from accelerometer at the hip and motion capture system was recorded during treadmill walking and running from 30 subjects in three different age groups: 10, 15, and >20 years old. Acceleration data was processed to ActiGraph counts with original band-pass filter at 1.66 Hz, to counts with wider filter at either 4 or 10 Hz, and to unfiltered acceleration according to “Euclidian norm minus one” (ENMO). Internal and external power, step frequency, and vertical displacement of center of mass (VD) were estimated from the motion capture data. Widening the frequency filter improved the relationship between higher locomotion speed and counts. It also removed age-group differences and decreased within-group variation. While ActiGraph counts were almost exclusively explained by VD, the counts from the 10 Hz filter were explained by VD and step frequency to an equal degree. In conclusion, a wider frequency filter improves assessment of physical activity intensity by more accurately capturing individual gait patterns.

scholarly journals Reexamination of Accelerometer Calibration with Energy Expenditure as Criterion: VO2net Instead of MET for Age-Equivalent Physical Activity Intensity

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3377 ◽  
Author(s):  
Daniel Arvidsson ◽  
Jonatan Fridolfsson ◽  
Christoph Buck ◽  
Örjan Ekblom ◽  
Elin Ekblom-Bak ◽  
...  
Keyword(s):  
Physical Activity ◽  
Age Groups ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Free Living ◽  
Activity Intensity ◽  
Age Related ◽  
Younger Age ◽  
Metabolic Equivalent Of Task ◽  
Adolescents And Adults

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO2net (VO2gross − VO2stand; mL⋅min−1⋅kg−1) as criterion compared to MET (VO2gross/VO2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speedeq) was used as indicator of the absolute speed (Speedabs) performed with the same effort in individuals of different body size/age. The results showed that VO2net was higher in younger age-groups for Speedabs, but was similar in the three age-groups for Speedeq. MET was lower in younger age-groups for both Speedabs and Speedeq. The same VO2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research.

Predicting Substance Use from Physical Activity Intensity in Adolescents

2004 ◽  
Vol 16 (3) ◽  
pp. 277-289 ◽  
Author(s):  
Kerri McCaul ◽  
Joseph Baker ◽  
John K. Yardley
Keyword(s):  
Physical Activity ◽  
Substance Use ◽  
Alcohol Use ◽  
Positive Relationship ◽  
Activity Type ◽  
Physical Activity Intensity ◽  
Individual Activity ◽  
Low Intensity ◽  
Activity Intensity ◽  
Negative Relationships

Adolescence is characterized as a period of change and adaptation typically marked by a decline in physical activity participation and accompanied by an increase in substance use. The purpose of this study was to examine the relationships among the type (team and individual activity) and intensity (high, medium, and low intensity) of physical activity and substance use (tobacco, marijuana, and alcohol use, and binge drinking) in a sample of 738 adolescents. Results indicated differing relationships among study variables depending on the type and intensity of physical activity and the type of substance used For instance, a positive relationship was found for physical activity intensity and alcohol use, but negative relationships were found for physical activity and tobacco and marijuana use. Collectively, the results reveal that the relationships between physical activity type and intensity and substance use are more complex than previously believed.

scholarly journals Validity of the Global Physical Activity Questionnaire (GPAQ) in Adult Latinas

2012 ◽  
Vol 9 (5) ◽  
pp. 698-705 ◽  
Author(s):  
Tracy Hoos ◽  
Nancy Espinoza ◽  
Simon Marshall ◽  
Elva M. Arredondo
Keyword(s):  
Physical Activity ◽  
Self Report ◽  
Accelerometer Data ◽  
Post Intervention ◽  
Activity Intensity ◽  
Time Work ◽  
Global Physical Activity Questionnaire ◽  
Work Commute ◽  
The Impact ◽  
Multilevel Intervention

Background:Valid and reliable self-report measures of physical activity (PA) are needed to evaluate the impact of interventions aimed at increasing the levels of PA. However, few valid measures for assessing PA in Latino populations exist.Objective:The purpose of this study is to determine whether the GPAQ is a valid measure of PA among Latinas and to examine its sensitivity to intervention change. Intervention attendance was also examined.Methods:Baseline and postintervention data were collected from 72 Latinas (mean age = 43.01; SD = 9.05) who participated in Caminando con Fe/Walking with Faith, a multilevel intervention promoting PA among church-going Latinas. Participants completed the GPAQ and were asked to wear the accelerometer for 7 consecutive days at baseline and again 6 months later. Accelerometer data were aggregated into 5 levels of activity intensity (sedentary, light, moderate, moderate-vigorous, and vigorous) and correlated to self-reported mean minutes of PA across several domains (leisure time, work, commute and household chores).Results:There were significant correlations at postintervention between self-reported minutes per week of vigorous LTPA and accelerometer measured vigorous PA (r = .404, P < .001) as well as significant correlations of sensitivity to intervention change (post intervention minus baseline) between self-reported vigorous LTPA and accelerometer-measured vigorous PA (r = .383, P < .003) and self-reported total vigorous PA and accelerometer measured vigorous PA (r = .363, P < .003).Conclusions:The findings from this study suggest that the GPAQ may be useful for evaluating the effectiveness of programs aimed at increasing vigorous levels of PA among Latinas.

scholarly journals Ability of RT3 Accelerometer Cut Points to Detect Physical Activity Intensity in Ambulatory Children With Cerebral Palsy

2014 ◽  
Vol 31 (4) ◽  
pp. 310-324 ◽  
Author(s):  
Jennifer Ryan ◽  
Michael Walsh ◽  
John Gormley
Keyword(s):  
Physical Activity ◽  
Cerebral Palsy ◽  
Vigorous Physical Activity ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Cut Points ◽  
Cut Point ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Children With Cerebral Palsy

This study investigated the ability of published cut points for the RT3 accelerometer to differentiate between levels of physical activity intensity in children with cerebral palsy (CP). Oxygen consumption (metabolic equivalents; METs) and RT3 data (counts/min) were measured during rest and 5 walking trials. METs and corresponding counts/min were classified as sedentary, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) according to MET thresholds. Counts were also classified according to published cut points. A published cut point exhibited an excellent ability to classify sedentary activity (sensitivity = 89.5%, specificity = 100.0%). Classification accuracy decreased when published cut points were used to classify LPA (sensitivity = 88.9%, specificity = 79.6%) and MVPA (sensitivity = 70%, specificity = 95–97%). Derivation of a new cut point improved classification of both LPA and MVPA. Applying published cut points to RT3 accelerometer data collected in children with CP may result in misclassification of LPA and MVPA.

scholarly journals A Comparison of Preschoolers’ Physical Activity Indoors versus Outdoors at Child Care

2018 ◽  
Vol 15 (11) ◽  
pp. 2463 ◽  
Author(s):  
Pooja Tandon ◽  
Brian Saelens ◽  
Chuan Zhou ◽  
Dimitri Christakis
Keyword(s):  
Physical Activity ◽  
Child Care ◽  
Preschool Age ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Preschool Age Children ◽  
Gps Devices ◽  
Outdoor Location ◽  
Indoor And Outdoor

The aims of this study were to quantify and examine differences in preschoolers’ indoor and outdoor sedentary time and physical activity intensity at child care using GPS devices and accelerometers. We conducted an observational study of 46 children (mean age 4.5 years, 30 boys, 16 girls) from five child care centers who wore accelerometers and GPS devices around their waists for five days during regular child care hours. GPS signal-to-noise ratios were used to determine indoor vs. outdoor location. Accelerometer data were categorized by activity intensity. Children spent, on average, 24% of child care time outdoors (range 12–37% by site), averaging 74 min daily outdoors (range 30–119 min), with 54% of children spending ≥60 min/day outdoors. Mean accelerometer activity counts were more than twice as high outdoors compared to indoors (345 (95) vs. 159 (38), (p < 0.001)), for girls and boys. Children were significantly less sedentary (51% of time vs. 75%) and engaging in more light (18% vs. 13%) and moderate-to-vigorous (MVPA) (31% vs. 12%) activity when outdoors compared to indoors (p < 0.001). To achieve a minute of MVPA, a preschooler needed to spend 9.1 min indoors vs. 3.8 min outdoors. Every additional 10 min outdoors each day was associated with a 2.9 min increase in MVPA (2.7 min for girls, 3.0 min for boys). Preschool-age children are twice as active and less sedentary when outdoors compared to indoors in child care settings. To help preschoolers achieve MVPA recommendations and likely attain other benefits, one strategy is to increase the amount of time they spend outdoors and further study how best to structure it.

scholarly journals Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Min-Cheol Kwon ◽  
Sunwoong Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Body Movement ◽  
Human Activity Recognition ◽  
Daily Activities ◽  
Whole Body ◽  
Wide Range ◽  
Artificial Neural

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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