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Updated: Jun 3, 2025
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
Published on: December 11, 2015
Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals
Jyotirmoy Nirupam Das1, Linying Ji2, Yuqi Shen3
1Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.
Goal And Aims:
One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes.
Focus Technology:
Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics).
Reference Technology:
The built-in nonwear sensor as "ground truth" to classify nonwear periods using other data, mimicking features of Actiwatch 2.
Sample:
Data were collected over 1week from employed adults (n = 853).
Design:
Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows.
Core Analytics:
The performance of the proposed algorithm was tested over 30-second epochs. Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features.
Core Outcomes:
The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms.
Important Supplemental Outcomes:
The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified.
Core Conclusion:
Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.
