Automatic Elderly Fall and Unstable Movement Detection System Using Framewise and LSTM Based Video Analytics on an Embedded Device
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Abstract
We introduce an edge processing device and cloud computation framework enabling activity profiling, unstable motion alerts, and fall alerts for elderly people living at home under their families’ care. The system analyzes video frames captured by fixed cameras, tracking each visible person and classifying their actions into one of nine ordinary activities, a fall, or unstable movement. Alert notifications are sent to caregivers if a fall or unstable movement is detected. The system comprises an embedded device (NVIDIA TX2) and cloud-based storage and analysis infrastructure. The main modules include video-based human detection, tracking, and recognition; fall detection; activity classification; and detection of painful, unstable, and confused motion likely to lead to falls. The system is designed for accuracy, usability, and cost. Individual module tests and a field test with a volunteer household indicate that the prototype system is ready for the next stage of commercial exploitation, with an accuracy of 91.6% for ordinary actions and falls as well as a recall of 97.02% for unstable motion.
Article Details
เนื้อหาหรือบทความที่ปรากฏในวารสารวิชาการ กสทช. เป็นลิขสิทธิ์ของสำนักงานคณะกรรมการกิจการกระจายเสียง กิจการโทรทัศน์ และกิจการโทรคมนาคมแห่งชาติ
(สำนักงาน กสทช.) ซึ่งสำนักงาน กสทช. ขอสงวนสิทธิ์มิให้นำเนื้อหา ข้อความ หรือบทความไม่ว่าทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ คัดลอก หรือตีพิมพ์ซ้ำโดยเด็ดขาด
เว้นแต่ได้รับอนุญาตเป็นลายลักษณ์อักษร ทั้งนี้ ข้อความ หรือความคิดเห็นที่ปรากฏในบทความแต่ละเรื่องที่ตีพิมพ์ในวารสารนี้ เป็นความคิดเห็นของผู้เขียน
ไม่ผูกพันต่อคณะกรรมการกิจการกระจายเสียง กิจการโทรทัศน์ และกิจการโทรคมนาคมแห่งชาติ (กสทช.) และสำนักงาน กสทช. รวมทั้งกรณีหากมี
การละเมิดสิทธิ์ถือเป็นความรับผิดชอบของผู้เขียนโดยตรงไม่เกี่ยวข้องกับสำนักงาน กสทช. แต่ประการใด
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