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An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

Zhen Li¹, Zhiqiang Wei, Yaofeng Yue

¹Department of Computer Science, Ocean University of China, Qingdao, China.

Journal of Medical Systems

|March 20, 2015

Summary

This study introduces a new method for human activity recognition using wearable sensors. The adaptive Hidden Markov Model (HMM) efficiently processes multi-sensor data for improved health and lifestyle analysis.

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Area of Science:

Biomedical Engineering
Computer Science
Human-Computer Interaction

Background:

Human activity recognition is crucial for personal health, wellness, and lifestyle studies.
Wearable multi-sensor devices are commonly used to collect human activity data.
Efficiently processing large datasets from these devices presents a significant challenge.

Purpose of the Study:

To present a novel technique for automatic activity recognition using multi-sensor data.
To address the big data challenge in wearable sensor data analysis.
To improve the robustness and efficiency of human activity recognition systems.

Main Methods:

An offline adaptive Hidden Markov Model (HMM) was proposed for efficient data utilization.
A sensor selection scheme was implemented using an improved Viterbi algorithm.
A new method incorporated personal experience as a priori information into the HMM.

Main Results:

The proposed method demonstrated superior robustness and efficiency compared to standard HMM and other methods.
Experiments were conducted using data from the eButton wearable computer.
The technique effectively processed multi-sensor data for activity recognition.

Conclusions:

The developed method offers a robust and efficient tool for evaluating human activity and lifestyle.
This approach enhances the utility of wearable multi-sensor devices for personal health monitoring.
The incorporation of personal experience improves the accuracy of activity recognition models.