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TinyFallNet: A Lightweight Pre-Impact Fall Detection Model
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DOI：10.3390/s23208459.png)
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摘要
En 中文
Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% < 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB > 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.
Keyword:
pre-impact fall detection
lightweight
ConvLSTM
TinyFallNet
AI总结
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors使用可穿戴惯性传感器检测老年人撞击前跌倒的大规模开放运动数据集 (KFall) 和基准算法OAAI
OAAI分享收藏A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults使用年轻人和老年人进行撞击前跌倒检测的不同类型算法的准确性和实用性的综合比较.png)
MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition用于方向和严重程度感知的跌倒检测和活动识别的深度卷积神经网络-XGBSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public DatasetSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning ApplicationsFallallld: 经典和深度学习应用的人类跌倒和日常生活活动的开放数据集IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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