Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors

Misaki, Shinya; Umakoshi, Keisuke; Matsui, Tomokazu; Choi, Hyuckjin; Fujimoto, Manato; Yasumoto, Keiichi

doi:10.1145/3427477.3429463

Cited by 8 publications

(1 citation statement)

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“…In our previous work [5], we have proposed a basic idea for this study with a simple test. Through this work, we extend and enhance our system for daily living activity recognition based on Doppler signals by redesigning hardware, intensive practical experiments, and performance comparisons.…”

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confidence: 99%

Location-Independent Doppler Sensing System for Device-Free Daily Living Activity Recognition

Misaki,

Yoshida,

Choi

et al. 2023

IEEE Access

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The recent advancements in sensing technology have opened up the possibilities for various services that support daily life, such as energy-saving home appliance control. To realize such services, accurate and cost-effective daily activity recognition in a wide range is essential. To actualize such a system, it is imperative to address the following requirements: the acquisition of sensors entails very high costs (Issue 1), it is hard to achieve precise recognition for location-independent activities like reading a book (Issue 2), a burden of wearing devices from the perspective of residents (Issue 3), and the preservation of residents' privacy is compromised by using image data from the camera (Issue 4). In this paper, we propose a method for recognizing daily living activities utilizing Doppler sensors in a relatively longer detection range than other motion detection sensors that can be used for dynamic objects. We assess the proposed system by optimizing recognition accuracy, evaluating ensemble methods, and examining sensor reduction's impact. In the first comparison, the logistic regression achieved the highest accuracy of 65.99% in the leave-one-person-out cross-validation. The second comparison revealed an accuracy of 59.39% for the parallel activity recognition method and 57.24% for the location estimation activity recognition method. In the third comparison, logistic regression achieved a recognition accuracy of 65.26% when four sensor nodes were used: two sensors were placed on both sides of a participant, another was diagonally behind the participant, and the other was installed on the ceiling. INDEX TERMSActivity recognition, Doppler sensor, machine learning, smart home applications I. INTRODUCTION

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confidence: 99%