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Sensors (Basel, Switzerland) Oct 2023The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer... (Review)
Review
The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.
Topics: Humans; Smartphone; Biomechanical Phenomena; Reproducibility of Results; Gait; Accelerometry
PubMed: 37896708
DOI: 10.3390/s23208615 -
International Journal of Environmental... Jun 2022The aim of this study was to compare the use of generic and cystic fibrosis (CF)-specific cut-points to assess movement behaviours in children and adolescents with CF....
The aim of this study was to compare the use of generic and cystic fibrosis (CF)-specific cut-points to assess movement behaviours in children and adolescents with CF. Physical activity (PA) was assessed for seven consecutive days using a non-dominant wrist-worn ActiGraph GT9X in 71 children and adolescents (36 girls; 13.5 ± 2.9 years) with mild CF. CF-specific and generic Euclidean norm minus one (ENMO) cut-points were used to determine sedentary time (SED), sleep, light physical activity (LPA), moderate physical activity and vigorous physical activity. The effect of using a CF-specific or generic cut-point on the relationship between PA intensities and lung function was determined. Movement behaviours differed significantly according to the cut-point used, with the CF-specific cut-points resulting in less time asleep (−31.4 min; p < 0.01) and in LPA (−195.1 min; p < 0.001), and more SED and moderate-to-vigorous PA (159.3 and 67.1 min, respectively; both p < 0.0001) than the generic thresholds. Lung function was significantly associated with LPA according to the CF-specific cut-points (r = 0.52; p = 0.04). Thresholds developed for healthy populations misclassified PA levels, sleep and SED in children and adolescents with CF. This discrepancy affected the relationship between lung function and PA, which was only apparent when using the CF-specific cut-points. Promoting LPA seems a promising strategy to enhance lung function in children and adolescents with CF.
Topics: Accelerometry; Adolescent; Child; Cystic Fibrosis; Exercise; Female; Humans; Sedentary Behavior; Sleep
PubMed: 35742382
DOI: 10.3390/ijerph19127133 -
Gait & Posture May 2017Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking... (Comparative Study)
Comparative Study
Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking human movement in domains that include sports, motion gaming, medicine, and wellness. The kinematic sensors can be used to estimate orientation, but can only estimate changes in position over short periods of time. We developed a prototype sensor that includes ultra wideband ranging sensors and kinematic sensors to determine the feasibility of fusing the two sensor technologies to estimate both orientation and position. We used a state space model and applied the unscented Kalman filter to fuse the sensor information. Our results demonstrate that it is possible to estimate orientation and position with less error than is possible with either sensor technology alone. In our experiment we obtained a position root mean square error of 5.2cm and orientation error of 4.8° over a 15min recording.
Topics: Accelerometry; Algorithms; Bayes Theorem; Biomechanical Phenomena; Equipment Design; Gravitation; Humans; Movement; Orientation; Proof of Concept Study; Robotics; Signal Processing, Computer-Assisted; Wearable Electronic Devices
PubMed: 28242567
DOI: 10.1016/j.gaitpost.2017.02.011 -
International Journal of Environmental... Mar 2022The aim of the current study was to provide new evidence for the associations between physical activity (PA), sedentary behavior (SB), and fear of falling (FOF) by...
The aim of the current study was to provide new evidence for the associations between physical activity (PA), sedentary behavior (SB), and fear of falling (FOF) by investigating the impact of replacing 30 min SB with both light-intensity PA (LPA) and moderate-to-vigorous PA (MVPA) on FOF in older Chinese women. Cross-sectional data from a Physical Activity and Health in Older Women Study (PAHIOWS) were analyzed for 1114 Chinese community-dwelling older women. Variables of focus were demographics, FOF, objectively measured PA and SB. Three different logistic models were used to examine the associations between PA, SB, and FOF (a single parameter model, a partition model and an isotemporal substitution). The results showed that reallocating 30 min/day of MVPA by SB was significantly associated with higher FOF (OR = 1.37; 95%CI: 1.04−1.79; p = 0.024), reallocating 30 min/day of SB by MVPA was significantly associated with a reduction of FOF (OR = 0.73; 95%CI: 0.56−0.96; p = 0.024). No significant associations were found between FOF with reallocating other activities by LPA and vice versa (p > 0.05). Subgroup analysis showed the isotemporal-substituted effects of MVPA and SB on FOF were stronger in older women with fall experience. In conclusion, the current findings showed that the increase of MVPA engagement and reduction of SB engagement may be most beneficial for FOF management and should be involved in public health guidelines, especially for older women with fall experience.
Topics: Accelerometry; Aged; Cross-Sectional Studies; Exercise; Fear; Female; Humans; Sedentary Behavior
PubMed: 35270631
DOI: 10.3390/ijerph19052938 -
BMC Research Notes Sep 2019To investigate the agreement between two data reduction approaches for detecting sedentary breaks from uni-axial accelerometry data collected in human participants....
OBJECTIVES
To investigate the agreement between two data reduction approaches for detecting sedentary breaks from uni-axial accelerometry data collected in human participants. Free-living, uni-axial accelerometer data (n = 318) were examined for sedentary breaks using two different methods (Healy-Matthews; MAH/UFFE). The data were cleaned and reduced using MAH/UFFE Analyzer software and custom Microsoft Excel macro's, such that the average daily sedentary break number were calculated for each data record, for both methods.
RESULTS
The Healy-Matthews and MAH/UFFE average daily break number correlated closely (R = 99.9%) and there was high agreement (mean difference: + 0.7 breaks/day; 95% limits of agreement: - 0.06 to + 1.4 breaks/day). A slight bias of approximately + 1 break/day for the MAH/UFFE Analyzer was evident for both the regression and agreement analyses. At a group level there were no statistically or practically significant differences within sample groups between the two methods.
Topics: Accelerometry; Adult; Algorithms; Cost-Benefit Analysis; Exercise; Female; Humans; Male; Middle Aged; Regression Analysis; Rural Population; Sedentary Behavior; Software; Urban Population
PubMed: 31511063
DOI: 10.1186/s13104-019-4606-4 -
International Journal of Environmental... Jan 2023The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by...
The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by PALbatch). Part 1: Thirty-five adults (23.4 ± 3.6 years) completed 12 laboratory activities (6 sedentary and 6 non-sedentary activities). Receiver operator characteristic (ROC) curves proposed optimal Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) cut-points of 26.4 mg (ENMO) and 30.1 mg (MAD). Part 2: Thirty-eight adults (22.6 ± 4.1 years) wore an activPAL during free-living. Estimates from PALbatch and MAD revealed a mean percent error (MPE) of 2.2%, mean absolute percent error (MAPE) of 6.5%, limits of agreement (LoA) of 19% with absolute and relative equivalence zones of 5% and 0.3 SD. Estimates from PALbatch and ENMO revealed an MPE of -10.6%, MAPE of 14.4%, LoA of 31% and 16% and 1 SD equivalence zones. After standing was isolated from sedentary behaviours, ROC analysis proposed an optimal cut-off of 21.9 mg (herein ENMOs). Estimates from PALbatch and ENMOs revealed an MPE of 3.1%, MAPE of 7.5%, LoA of 25% and 9% and 0.5 SD equivalence zones. The MAD and ENMOs cut-points performed best in discriminating between sedentary and non-sedentary activity during free-living.
Topics: Accelerometry; Humans; Adult; Exercise; Sedentary Behavior; Young Adult; Fitness Trackers
PubMed: 36767662
DOI: 10.3390/ijerph20032293 -
Medicine and Science in Sports and... May 2015The purpose of this study was to develop and validate methods for analyzing wrist accelerometer data in youth.
PURPOSE
The purpose of this study was to develop and validate methods for analyzing wrist accelerometer data in youth.
METHODS
A total of 181 youth (mean ± SD; age, 12.0 ± 1.5 yr) completed 30 min of supine rest and 8 min each of 2 to 7 structured activities, selected from a list of 25. Receiver operating characteristic (ROC) curves and regression analyses were used to develop prediction equations for energy expenditure (child-METs; measured activity V˙O2 divided by measured resting V˙O2) and cut points for computing time spent in sedentary behaviors (SB), light (LPA), moderate (MPA), and vigorous (VPA) physical activity. Both vertical axis (VA) and vector magnitude (VM) counts per 5 s were used for this purpose. The validation study included 42 youth (age, 12.6 ± 0.8 yr) who completed approximately 2 h of unstructured PA. During all measurements, activity data were collected using an ActiGraph GT3X or GT3X+, positioned on the dominant wrist. Oxygen consumption was measured using a Cosmed K4b. Repeated-measures ANOVA were used to compare measured versus predicted child-METs (regression only) and time spent in SB, LPA, MPA, and VPA.
RESULTS
All ROC cut points were similar for area under the curve (≥0.825), sensitivity (≥0.756), and specificity (≥0.634), and they significantly underestimated LPA and overestimated VPA (P < 0.05). The VA and VM regression models were within ±0.21 child-METs of mean measured child-METs and ±2.5 min of measured time spent in SB, LPA, MPA, and VPA, respectively (P > 0.05).
CONCLUSIONS
Compared to measured values, the VA and VM regression models developed on wrist accelerometer data had insignificant mean bias for child-METs and time spent in SB, LPA, MPA, and VPA; however, they had large individual errors.
Topics: Accelerometry; Adolescent; Calorimetry, Indirect; Child; Energy Metabolism; Female; Humans; Male; Motor Activity; ROC Curve; Regression Analysis; Reproducibility of Results
PubMed: 25207928
DOI: 10.1249/MSS.0000000000000502 -
Journal of Gerontological Nursing May 2022Accurate mobility assessment of hospitalized older adults is necessary to aid nurses in planning and providing appropriate mobility support; however, nurses cite lack of...
Accurate mobility assessment of hospitalized older adults is necessary to aid nurses in planning and providing appropriate mobility support; however, nurses cite lack of resources and time limitations as barriers. Accelerometry enables a detailed objective measurement of predominant hospital mobility activities in the older adult population, such as percent time sitting, and the sit-to-stand (STS) transition. The current exploratory study examined the use of a novel, unobtrusive accelerometry technique to obtain postural and STS metrics on 27 older adults during their hospital stay. Total device wear time in the hospital was 96.2%. Participants spent 60.3% time lying, 20.3% time sitting, 5.3% time standing, and 2% time stepping during hospitalization, and, on average, completed the STS transition 20 times ( = 13) per 24-hour period. There were no participant complaints about wearing the device. Our exploratory study shows accelerometry provides automated, continuous data and may support accurate nursing assessment of patient mobility. [(5), 35-41.].
Topics: Accelerometry; Aged; Hospitalization; Humans; Length of Stay
PubMed: 35511066
DOI: 10.3928/00989134-20220405-02 -
Animal : An International Journal of... Jul 2021Understanding broiler behaviours provides important implications for animal well-being and farm management. The objectives of this study were to classify specific...
Understanding broiler behaviours provides important implications for animal well-being and farm management. The objectives of this study were to classify specific broiler behaviours by analysing data from wearable accelerometers using two machine learning models, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). Lightweight triaxial accelerometers were used to record accelerations of nine 7-week-old broilers at a sampling frequency of 40 Hz. A total of 261.6-min data were labelled for four behaviours - walking, resting, feeding and drinking. Instantaneous motion features including magnitude area, vector magnitude, movement variation, energy, and entropy were extracted and stored in a dataset which was then segmented by one of the six window lengths (1, 3, 5, 7, 10 and 20 s) with 50% overlap between consecutive windows. The mean, variation, SD, minimum and maximum of each instantaneous motion feature and two-way correlations of acceleration data were calculated within each window, yielding a total of 43 statistic features for training and testing of machine learning models. Performance of the models was evaluated using pure behaviour datasets (single behaviour type per dataset) and continuous behaviour datasets (continuous recording that involved multiple behaviour types per dataset). For pure behaviour datasets, both KNN and SVM models showed high sensitivities in classifying broiler resting (87% and 85%, respectively) and walking (99% and 99%, respectively). The accuracies of SVM were higher than KNN in differentiating feeding (88% and 75%, respectively) and drinking (83% and 62%, respectively) behaviours. Sliding window with 1-s length yielded the best performance for classifying continuous behaviour datasets. The performance of classification model generally improved as more birds were included for training. In conclusion, classification of specific broiler behaviours can be achieved by recording bird triaxial accelerations and analysing acceleration data through machine learning. Performances of different machine learning models differ in classifying specific broiler behaviours.
Topics: Accelerometry; Animals; Behavior, Animal; Chickens; Machine Learning; Support Vector Machine
PubMed: 34102430
DOI: 10.1016/j.animal.2021.100269 -
Sensors (Basel, Switzerland) Jan 2024Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor...
Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere-Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere-Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.
Topics: Humans; Female; Aged; Aged, 80 and over; Accelerometry; Exercise; Thigh; Wrist; Algorithms
PubMed: 38339613
DOI: 10.3390/s24030895