ArticleSensors (Basel, Switzerland)2020
Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research From 2011 to 2023.Journal of medical Internet research · 2024Pooled it
- A Systematic Review of Wearable Sensors for Monitoring Physical Activity.Sensors (Basel, Switzerland) · 2022Pooled it
- Partnering a Compensatory Application with Activity-Aware Prompting to Improve Use in Individuals with Amnestic Mild Cognitive Impairment: A Randomized Controlled Pilot Clinical Trial.Journal of Alzheimer's disease : JAD · 2022Trial
- A Battery-Aware Sensor Fusion Strategy: Unifying Magnetic-Inertial Attitude and Power for Energy-Constrained Motion Systems.Sensors (Basel, Switzerland) · 2026Article
- Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.Methods of information in medicine · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.Sensors (Basel, Switzerland) · 2022Review
- Wearable devices for continuous monitoring of biosignals: Challenges and opportunities.APL bioengineering · 2022Review
- Energy Autonomous Sweat-Based Wearable Systems.Advanced materials (Deerfield Beach, Fla.) · 2021Review
- Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores.IEEE access : practical innovations, open solutions · 2021Article
- Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020Review
- Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.