Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization

= 7) evaluated the system against ground truth comprising activPAL3 accelerometry and video analysis. Across 378 postural events, the sensor achieved high temporal fidelity (mean absolute error < 1.6 s) and 100% sensitivity in counting exercise repetitions. The system differentiated workstation occupancy from physical absence. These findings demonstrate that ToF sensing matches the accuracy of video analysis without privacy concerns while offering the contextual awareness required for just-in-time, adaptive workplace interventions.

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Publication Details

Journal
Sensors
Published
2026-04-17
DOI
https://doi.org/10.3390/s26082468
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization

Ananda Maiti, Anjia Ye, Matthew Schmidt, Scott J. Pedersen
Sensors
Context-Aware Activity Recognition Systems
article

Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization

Ananda Maiti, Anjia Ye, Matthew Schmidt, Scott J. Pedersen
article en

Abstract

= 7) evaluated the system against ground truth comprising activPAL3 accelerometry and video analysis. Across 378 postural events, the sensor achieved high temporal fidelity (mean absolute error < 1.6 s) and 100% sensitivity in counting exercise repetitions. The system differentiated workstation occupancy from physical absence. These findings demonstrate that ToF sensing matches the accuracy of video analysis without privacy concerns while offering the contextual awareness required for just-in-time, adaptive workplace interventions.

SensorsVol. 26(8)
University of Tasmania (AU), Deakin University (AU)
Openalex Percentile: Top 22%
Context-Aware Activity Recognition Systems
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