Timely and reliable detection of falls is a large and rapidly growing field of research due to the medical and financial demand of caring for a constantly growing elderly population. Within the past 2 decades, the availability of high-quality hardware (high-quality sensors and AI microchips) and software (machine learning algorithms) technologies has served as a catalyst for this research by giving developers the capabilities to develop such systems. This study developed multiple application components in order to investigate the development challenges and choices for fall detection systems, and provide materials for future research. The smart application developed using this methodology was validated by the results from fall detection modelling experiments and model mobile deployment. The best performing model overall was the ResNet152 on a standardised, and shuffled dataset with a 2s window size which achieved 92.8% AUC, 7.28% sensitivity, and 98.33% specificity. Given these results it is evident that accelerometer and ECG sensors are beneficial for fall detection, and allow for the discrimination between falls and other activities. This study leaves a significant amount of room for improvement due to weaknesses identified in the resultant dataset. These improvements include using a labelling protocol for the critical phase of a fall, increasing the number of dataset samples, improving the test subject representation, and experimenting with frequency domain preprocessing.
翻译:及时可靠地探测瀑布是一个庞大和迅速增长的研究领域,原因是需要照顾不断增长的老年人口,这是一个医疗和资金需求,因此需要照顾不断增长的老年人。在过去20年中,高质量的硬件(高质量的传感器和AI微型芯片)和软件(机器学习算法)技术的提供,通过使开发者具备开发这种系统的能力,成为这一研究的催化剂。该研究开发了多种应用组成部分,以调查跌落探测系统的发展挑战和选择,并为今后的研究提供材料。利用这一方法开发的智能应用得到了秋季探测模型实验和模型移动部署的结果的验证。最佳的模型是标准化的ResNet152和拆卸的2个窗口尺寸数据集,达到92.8%的ACUC、7.28%的灵敏度和98.33%的特性。鉴于这些结果,很明显,加速计和ECG传感器有利于跌落探测,并允许跌落与其他活动之间有区别。由于结果数据集的薄弱环节,这一研究留下很大的改进空间。这些改进包括使用一个标签协议,用于关键的频度测试的域,改进了数据采集过程,增加了数据采集的频率。