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Smartphone-Based Patients’ Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring

  • Patient Facing Systems
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Abstract

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients’ activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

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Acknowledgments

This research is sponsored by National Natural Science Foundation of China (No.61401029, 61171014, 61472044, 61472403, 61371185), Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004), the Fundamental Research Funds for the Central Universities (No.2012LYB46), and Beijing Youth Excellence Program (YETP0296).

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Authors and Affiliations

  1. Beijing Advanced Innovation Center for Future Education, Beijing Normal University, Beijing, People’s Republic of China

    Junqi Guo

  2. College of Information Science and Technology, Beijing Normal University, Beijing, People’s Republic of China

    Junqi Guo, Xi Zhou, Yunchuan Sun, Guoxing Zhao & Zhuorong Li

  3. Peking University Third Hospital Geriatrics of Internal Department, Peking University Third Hospital, Beijing, People’s Republic of China

    Gong Ping

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  1. Junqi Guo
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  2. Xi Zhou
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  3. Yunchuan Sun
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  4. Gong Ping
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  5. Guoxing Zhao
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  6. Zhuorong Li
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Corresponding author

Correspondence to Gong Ping.

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This article is part of the Topical Collection on Patient Facing Systems

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Guo, J., Zhou, X., Sun, Y. et al. Smartphone-Based Patients’ Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring. J Med Syst 40, 140 (2016). https://doi.org/10.1007/s10916-016-0497-2

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