Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.

The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.

Lendt C ，Braun T ，Biallas B ，Froböse I ，Johansson PJ ... -  《International Journal of Behavioral Nutrition and Physical Activity》

Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children.

Lendt C ，Hettiarachchi P ，Johansson PJ ，Duncan S ，Lund Rasmussen C ，Narayanan A ，Stewart T ... -  《-》

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Lendt C ，Hansen N ，Froböse I ，Stewart T ... -  《International Journal of Behavioral Nutrition and Physical Activity》

Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion.

Steeves JA ，Bowles HR ，McClain JJ ，Dodd KW ，Brychta RJ ，Wang J ，Chen KY ... -  《-》

Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer.

Hip-worn accelerometers are widely used to estimate physical activity (PA), but the accuracy of acceleration threshold-based analysis is compromised when it comes to identifying stationary and sedentary behaviors, let alone classifying body postures into lying, sitting, or standing. The purpose of this study was to devise a novel method for accurate classification of body posture using triaxial data from hip-worn accelerometer and to evaluate its performance in free-living conditions against a thigh-worn accelerometer. The posture classification rested on 2 facts: constant Earth's gravity vector and upright walking posture. Thirty healthy adults wore a hip-mounted accelerometer and underwent an array of lying, sitting, standing, and walking tasks. Task type, their order, and length were randomly assigned to each participant. During walking, the accelerometer orientation in terms of gravity vector was taken as reference, and the angle for posture estimation (APE) was determined from the incident accelerometer orientation in relation to the reference vector. Receiver operating characteristic (ROC) curve yielded an optimal cut-point APE of 64.9° (sensitivity 100% and specificity 100%) for lying and sitting and 11.6° (94.2%; 94.5%) for sitting and standing. In free-living conditions, high agreement (89.2% for original results and 90.4% for median-filtered results) in identifying sedentary periods (sitting and lying) was observed between the results from hip- and thigh-worn accelerometers. Walking provides a valid reference activity to determine the body posture. The proposed APE analysis of the raw data from hip-worn triaxial accelerometer gives accurate and specific information about daily times spent lying, sitting, and standing.

Vähä-Ypyä H ，Husu P ，Suni J ，Vasankari T ，Sievänen H ... -  《-》