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.

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 Change Point Determination in Audio Data Using Auditory Features

2015 ◽  
Vol 61 (2) ◽  
pp. 185-190 ◽  
Author(s):  
Tomasz Maka
Keyword(s):  
Change Point ◽  
Feature Space ◽  
Change Point Detection ◽  
Detection Scheme ◽  
Detection Techniques ◽  
Detection Process ◽  
Feature Spaces ◽  
Audio Data ◽  
A New Technique ◽  
Point Detection

Abstract The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ΔBIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ΔBIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ΔBIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ΔBIC scheme with the MFCC feature space.

scholarly journals The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 294-304
Author(s):  
Luis A. Barboza ◽  
Paola Vásquez ◽  
Gustavo Mery ◽  
Fabio Sanchez ◽  
Yury E. García ◽  
...  
Keyword(s):  
Costa Rica ◽  
Change Point ◽  
Human Mobility ◽  
Change Point Detection ◽  
Population Mobility ◽  
Detection Techniques ◽  
Community Transmission ◽  
Parametric Change ◽  
Point Detection

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.

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