Ekerete Idongesit F, Garcia-Constantino M, Diaz Yohanca, Giggins Oonagh M, Mustafa M A, Konios Alexandras, Pouliet Pierre, Nugent Chris D, McLaughlin Jim
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5357-5361. doi: 10.1109/EMBC44109.2020.9175896.
This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.
本文提出融合来自非侵入式传感解决方案的数据，用于家庭环境中活动的识别和分类。活动识别和分类能力有助于客观监测老年人的健康和 wellness 趋势。虽然使用视频和立体摄像头监测活动能提供充分的洞察，但用户隐私并未得到充分保护（即，用户很容易从图像中被识别出来）。另一个问题是，广泛使用的可穿戴传感器，如加速度计，存在一些缺点，如电池续航有限、采用问题和可穿戴性问题。本研究调查了使用低成本热传感解决方案，该方案能够生成带有时间戳的独特热斑，以识别研究参与者的活动。在实验室厨房中放置两个热传感器，从10名健康参与者那里记录了超过11000个热斑：（i）一个安装在天花板上，（ii）另一个放置在房间角落的迷你三脚架上。此外，将天花板热传感器的数据与从侧面热传感器收集的数据进行了融合。在数据采集的每个阶段都使用接触式传感器作为时间戳近似的金标准，这使得能够获得：（i）每项活动发生的时间，（ii）执行的活动类型，以及（iii）每个参与者的位置。实验结果表明，基于聚类的活动识别和分类取得了成功，测试聚类和特征的平均回归系数为0.95。此外，在评估测试中，k近邻、逻辑回归、神经网络和随机森林等数据挖掘模型的平均准确率达到了95%。临床相关性——本研究提出了一种用于活动识别和分类的非侵入式（即隐私友好型）解决方案，用于分析健康和幸福趋势。
