Fall behavior detection algorithm for the elderly based on AlphaPose optimization model

doi:10.11772/j.issn.1001-9081.2021020331

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 294-301.DOI: 10.11772/j.issn.1001-9081.2021020331

• Frontier and comprehensive applications • Previous Articles Next Articles

Fall behavior detection algorithm for the elderly based on AlphaPose optimization model

Jingqi MA¹, Huan LEI¹, Minyi CHEN²()

^1.Guangdong Key Laboratory of Modern Control Technology （Institute of Intelligent Manufacturing，Guangdong Academy of Sciences），Guangzhou Guangdong 510070，China
^2.Guangdong Institute of Scientific and Technical Information，Guangzhou Guangdong 510070，China

Received:2021-03-05 Revised:2021-05-17 Accepted:2021-05-18 Online:2021-06-11 Published:2022-01-10
Contact: Minyi CHEN
About author:MA Jingqi， born in 1988， M. S.， engineer. His research interests include behavior analysis， deep learning.
LEI Huan， born in 1987， M. S.， engineer. His research interests include deep learning， image recognition.
CHEN Minyi， born in 1986， M. S.， research assistant. His research interests include deep learning， big data analysis.
Supported by:
Guangzhou Science and Technology Program(202007040007)

基于AlphaPose优化模型的老人跌倒行为检测算法

马敬奇¹, 雷欢¹, 陈敏翼²()

^1.广东省现代控制技术重点实验室（广东省科学院智能制造研究所），广州 510070
^2.广东省科学技术情报研究所，广州 510070

通讯作者: 陈敏翼
作者简介:马敬奇（1988—），男，河南安阳人，工程师，硕士，主要研究方向：行为分析、深度学习
雷欢（1987—），男，湖南衡阳人，工程师，硕士，主要研究方向：深度学习、图像识别
陈敏翼（1986—），男，广东潮州人，助理研究员，硕士，主要研究方向：深度学习、大数据分析。
基金资助:
广州市科技计划项目(202007040007)

Abstract

Abstract:

In order to detect the elderly fall high-risk behaviors quickly and accurately on the low-power and low-cost hardware platform， an abnormal behavior detection algorithm based on AlphaPose optimization model was proposed. Firstly， the pedestrian target detection model and pose estimation model were optimized to accelerate the human target detection and pose joint point reasoning. Then， the image coordinate data of human pose joint points were computed rapidly through the optimized AlphaPose model. Finally， the relationship between the head joint point linear velocity and the crotch joint linear velocity at the moment the human body falls was calculated， as well as the change of the angle between the midperpendicular of the torso and X-axis of the image， were calculated to determine the occurrence of the fall. The proposed algorithm was deployed to the Jetson Nano embedded development board， and compared with several main fall detection algorithms based on human pose at present： YOLO （You Only Look Once）v3+Pose， YOLOv4+Pose， YOLOv5+Pose， trt_pose and NanoDet+Pose. Experimental results show that on the used embedded platform when the image resolution is 320×240， the proposed algorithm has the detection frame rate of 8.83 frame/s and the accuracy of 0.913， which are both better than those of the algorithms compared above. The proposed algorithm has relatively high real-time performance and accuracy， and can timely detect the occurrence of the elderly fall behaviors.

Key words: real-time fall detection, pose estimation, pose joint point, embedded platform, target detection, deep learning

摘要：

针对在低功耗、低成本硬件平台快速准确检测老人跌倒高危行为的问题，提出了一种基于AlphaPose优化模型的老人异常行为检测算法。首先，对行人目标检测模型和姿态估计模型进行优化，以加快人体目标检测和姿态关节点推理；然后，通过优化的AlphaPose模型快速计算得到人体姿态关节点图像坐标数据；最后，计算人体跌倒瞬间头部关节点线速度与胯部关节线速度之间的关系，以及人体中垂线与图像X轴之间夹角的变化来判断跌倒现象的发生。将所提算法移植到Jetson Nano嵌入式开发板上，并与当前主要的基于人体姿态的跌倒检测算法YOLOv3+Pose、YOLOv4+Pose、YOLOv5+Pose、trt_pose和NanoDet+Pose进行对比分析。实验结果表明，在所用嵌入式平台上，图像分辨率为320×240时，所提算法的检测帧率达到8.83 frame/s，准确率为0.913，均优于对比算法。该算法具有较高的实时性和准确率，能够及时检测老人跌倒行为的发生。

关键词: 实时跌倒检测, 姿态估计, 姿态关节点, 嵌入式平台, 目标检测, 深度学习

CLC Number:

TP391.4

Jingqi MA, Huan LEI, Minyi CHEN. Fall behavior detection algorithm for the elderly based on AlphaPose optimization model[J]. Journal of Computer Applications, 2022, 42(1): 294-301.

马敬奇, 雷欢, 陈敏翼. 基于AlphaPose优化模型的老人跌倒行为检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 294-301.

Figures/Tables 10

References 19

1	黄明安，陈钰. 中国人口老龄化的现状及建设［J］. 经济研究导刊， 2018（10）：60-64， 72. 10.2196/preprints.27345
	HUANG M A， CHEN Y. The status quo and advice of China’s ageing［J］. Economic Research Guide， 2018（10）：60-64， 72. 10.2196/preprints.27345
2	陈瑜，姜小鹰，杨美丽，等. 养老机构老年人跌倒效能状况及其影响因素分析［J］. 中国实用护理， 2016， 32（6）：412-415. 10.3760/cma.j.issn.1672-7088.2016.06.004
	CHEN Y， JIANG X Y， YANG M L， et al. lnvestigation on the fall efficacy of elderly people in nursing homes and its influencing factors［J］. Chinese Journal of Practical Nursing， 2016， 32（6）：412-425. 10.3760/cma.j.issn.1672-7088.2016.06.004
3	PIERLEONI P， BELLI A， PALMA L， et al. A high reliability wearable device for elderly fall detection［J］. IEEE Sensors Journal， 2015， 15（8）：4544-4553. 10.1109/jsen.2015.2423562
4	KHANM S， YU M， FENG P M， et al. An unsupervised acoustic fall detection system using source separation for sound interference suppression［J］. Signal Processing， 2015， 110：199-210. 10.1016/j.sigpro.2014.08.021
5	SUCERQUIA A， LÓPEZ J D， VARGAS-BONILLA J F. Real-life/real-time elderly fall detection with a triaxial accelerometer［J］. Sensors， 2018， 18（4）： No.1101. 10.3390/s18041101
6	CHEN K H， HSU Y W， YANG J J， et al. Enhanced characterization of an accelerometer-based fall detection algorithm using a repository［J］. Instrumentation Science and Technology， 2017， 45（4）：382-391. 10.1080/10739149.2016.1268155
7	朱艳，张亚萍，利曙生，等. 基于深度视觉传感器和卷积神经网络的跌倒检测算法［J］. 光学技术， 2021， 47（1）：56-61.
	ZHU Y， ZHANG Y P， LI S S， et al. Fall detection algorithm based on depth vision sensor and convolution neural network［J］. Optical Technique， 2021， 47（1）：56-61.
8	SALEH M， LE BOUQUIN JEANNÈS R. Elderly fall detection using wearable sensors： a low cost highly accurate algorithm［J］. IEEE Sensors Journal， 2019， 19（8）：3156-3164. 10.1109/jsen.2019.2891128
9	郭欣，王红豆，孙连浩，等. 基于改进姿态估计算法的嵌入式平台实时跌倒检测［J］. 科学技术与工程， 2020， 20（30）：12500-12506. 10.3969/j.issn.1671-1815.2020.30.035
	GUO X， WANG H D， SUN L H， et al. Real-time fall detection for embedded platform based on improved pose estimation algorithm［J］. Science Technology and Engineering， 2020， 20（30）：12500-12506. 10.3969/j.issn.1671-1815.2020.30.035
10	于乃功，柏德国. 基于姿态估计的实时跌倒检测算法［J］. 控制与决策， 2020， 35（11）：2761-2766. 10.13195/j.kzyjc.2019.0382
	YU N G， BAI D G. Real-time fall detection algorithm based on pose estimation［J］. Control and Decision， 2020， 35（11）：2761-2766. 10.13195/j.kzyjc.2019.0382
11	CAO Z， SIMON T， WEI S E， et al. Realtime multi-person 2D pose estimation using part affinity fields［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2017：1302-1310. 10.1109/cvpr.2017.143
12	MA C， SHIMADA A， UCHIYAMA H， et al. Fall detection using optical level anonymous image sensing system［J］. Optics and Laser Technology， 2019， 110：44-61. 10.1016/j.optlastec.2018.07.013
13	DE MIGUEL K， BRUNETE A， HERNANDO M， et al. Home camera-based fall detection system for the elderly［J］. Sensors， 2017， 17（12）： No.2864. 10.3390/s17122864
14	曹建荣，吕俊杰，武欣莹，等. 融合运动特征和深度学习的跌倒检测算法［J］. 计算机应用， 2021， 41（2）：583-589.
	CAO J R， LYU J J， WU X Y， et al. Fall detection algorithm integrating motion features and deep learning［J］. Journal of Computer Applications， 2021， 41（2）：583-589.
15	NÚÑEZ-MARCOS A， AZKUNE G， ARGANDA-CARRERAS I. Vision-based fall detection with convolutional neural networks［J］. Wireless Communications and Mobile Computing， 2017， 2017： No.9474806. 10.1155/2017/9474806
16	GAMMULLE H， DENMAN S， SRIDHARAN S， et al. Two stream LSTM： a deep fusion framework for human action recognition［C］// Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision. Piscataway： IEEE， 2017：177-186. 10.1109/wacv.2017.27
17	FANG H S， XIE S Q， TAI Y W， et al. RMPE： regional multi-person pose estimation［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017：2353-2362. 10.1109/iccv.2017.256
18	赵雪章，丁犇，席运江. 视频监控下利用改进型YOLOv3的弱监督式异常行为检测［J］. 光学技术， 2021， 47（1）：120-128.
	ZHAO X Z， DING B， XI Y J. Weak supervised abnormal behavior detection using improved YOLOv3 under video surveillance［J］. Optical Technique， 2021， 47（1）：120-128.
19	CHARFI I， MITERAN J， DUBOIS J， et al. Optimized spatiotemporal descriptors for real-time fall detection： comparison of support vector machine and Adaboost-based classification［J］. Journal of Electronic Imaging， 2013， 22（4）： No.041106. 10.1117/1.jei.22.4.041106

模型	AP@0.5：0.95	AP@0.5	AP@0.75
OpenPose	61.8	84.9	67.5
Detectron	67.0	88.0	73.1
AlphaPose	73.3	89.2	79.1

模型	AP@0.5：0.95	AP@0.5	AP@0.75
OpenPose	61.8	84.9	67.5
Detectron	67.0	88.0	73.1
AlphaPose	73.3	89.2	79.1

目标检测模型	帧率/ （frame·s^-1）	平均准确率/%	图像分辨率
YOLOv4-tiny-416	10.70	84.6	320×240
YOLOv4-tiny-trt-416（本文）	30.30	84.6	320×240
NanoDet-m	8.33	56.3	320×240
YOLOv5s	5.78	83.4	320×240
YOLOv3-spp-416	4.15	86.7	320×240

目标检测模型	帧率/ （frame·s^-1）	平均准确率/%	图像分辨率
YOLOv4-tiny-416	10.70	84.6	320×240
YOLOv4-tiny-trt-416（本文）	30.30	84.6	320×240
NanoDet-m	8.33	56.3	320×240
YOLOv5s	5.78	83.4	320×240
YOLOv3-spp-416	4.15	86.7	320×240

姿态估计模型	帧率/（frame·s^-1）	平均准确率/%	图像分辨率
Pose	7.35	71.2	320×240
OpenPose	3.48	60.5	320×240
trt_pose	6.93	55.1	224×224
Pose-trt（本文）	12.50	71.2	320×240

Fall behavior detection algorithm for the elderly based on AlphaPose optimization model

基于AlphaPose优化模型的老人跌倒行为检测算法

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Metrics

算法	漏检数	误检数	瞬时跌倒特征次数	跌倒状态特征次数	准确率	帧率/（frame·s^-1）	视频分辨率
YOLOv3-spp-416+Pose	16	9	6	78	0.759	3.05	320×240
YOLOv4-tiny-416+Pose	8	3	7	93	0.894	4.38	320×240
YOLOv5s+Pose	7	3	11	94	0.903	2.73	320×240
trt_pose	17	10	3	77	0.634	4.31	320×240
NanoDet-m+Pose	13	11	8	78	0.769	2.52	320×240
本文算法	6	3	12	95	0.913	8.83	320×240

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