Using Genetic Algorithms for Optimal Change Point Detection in Activity Monitoring

2016 ◽  
Author(s):  
Naveed Khan ◽  
Sally McClean ◽  
Shuai Zhang ◽  
Chris Nugent
Keyword(s):  
Genetic Algorithms ◽  
Change Point ◽  
Activity Monitoring ◽  
Change Point Detection ◽  
Point Detection ◽  
Optimal Change

scholarly journals Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

Sensors ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 1784 ◽  
Author(s):  
Naveed Khan ◽  
Sally McClean ◽  
Shuai Zhang ◽  
Chris Nugent
Keyword(s):  
Genetic Algorithms ◽  
Change Point ◽  
Optimal Parameter ◽  
Activity Monitoring ◽  
Change Point Detection ◽  
Point Detection

scholarly journals Optimal change point detection in Gaussian processes

2018 ◽  
Vol 193 ◽  
pp. 151-178 ◽  
Author(s):  
Hossein Keshavarz ◽  
Clayton Scott ◽  
XuanLong Nguyen
Keyword(s):  
Gaussian Processes ◽  
Change Point ◽  
Change Point Detection ◽  
Point Detection ◽  
Optimal Change

scholarly journals Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 310 ◽  
Author(s):  
Cristian Culman ◽  
Samaneh Aminikhanghahi ◽  
Diane J. Cook
Keyword(s):  
Change Point ◽  
Recognition Performance ◽  
Sampling Rate ◽  
Activity Monitoring ◽  
Change Point Detection ◽  
Continuous Sampling ◽  
Detection Techniques ◽  
Transition Times ◽  
Point Detection ◽  
Complex Activities

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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