S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Local Object Tracking Using Infrared Array for Bed-exit Behavior Recognition

Cheng-Jian Lin, Chi-Huang Shih, Ta-Sen Wei, Peng-Ta Liu, and Ching-Yu Shih

(Received September 6, 2021; Accepted December 20, 2021)

Keywords: behavior recognition, object tracking, bed exit, infrared sensor array

Bed-exit behavior recognition can be the first line of defense to prevent a subsequent fall and injuries, especially for patients with a high fall risk. The techniques adopted to recognize bed-exit behavior include sensor- and vision-based processing. Generally, vision-based techniques can obtain a wide range of activity information to ensure a good recognition performance. Privacy concerns, however, impede the potential use of vision-based techniques and require the monitoring of activities in only a limited region. This paper focuses on behavior analysis using sensor-based techniques to deal with privacy concerns and other practical issues such as environmental cleaning and behavior differentiation between patients and caregivers. A local object tracking (LOT) technique based on an array of multiple reflective infrared (IR) sensors is developed to monitor user activities in a limited region. The proposed IR-based LOT technique utilizes a finite state machine (FSM) to differentiate the bed-exit activities from caregiver and in-bed user activities. Furthermore, this bed-exit recognition system is realized as a product prototype to examine its performance in a real ward environment. The experimental results show a correct recognition rate of 99% for 26 bedside activities, four of which are caregiver activities, 16 of which are the everyday activities of the in-bed patient, and six of which are bed-exit activities. In a satisfaction survey conducted at a medical institution, 89% of participants (33 caregivers and 22 patients) considered the system to be effective and 90% of them were satisfied with the quality of the bed-exit recognition prototype.

Corresponding author: Chi-Huang Shih

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