在日常生活环境评价一个智能手机为基础的人类活动识别系统
概要
A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.
Abstract
An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors).
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.
Introduction
无处不在的感知已经成为一个引人入胜的研究领域，由于日益强大，体积小，成本低的计算和传感设备1。使用可穿戴式传感器移动度监视已经产生了极大的兴趣，因为消费级微电子能够检测高精度地1的运动特性。使用可穿戴式传感器人体行为识别（HAR）是最近的一个研究领域，在20世纪80年代进行的，90年代2的初步研究– 4。
现代智能手机包含必要的传感器和实时计算能力的移动性活动识别。在设备上的实时分析允许活动分类和数据上传，而无需用户或研究者干预。与流动性分析软件的智能手机可以提供健身追踪，健康监测，跌倒检测，家庭或工作的自动化，自我管理练习的研究E中5。智能手机也算是惯性测量平台，用于检测人类的移动活动和移动模式，采用板载传感器输出6计算数学产生的信号特征。共同特点生成方法包括启发式，时域，频域和小波分析为基础的方法7。
检测指定的活动1,5,6,7当现代智能手机HAR系统显示较高的预测精度。这些研究中评价方法以及精度发生变化，因为大多数的研究有自己的训练组，环境设置，和数据收集协议。灵敏度，特异性，准确性，召回，精度，和F-分数通常用于描述预测质量。然而，几乎没有任何信息可以在对检测实时的活动变化的能力“并发活动”的认可和评价方法1，对于试图进行分类的几项活动HAR系统。评估方法HAR系统的精度研究之间有很大差异。不管是分类算法和应用的特点，中金标准评估方法的描述是模糊的大多数HAR研究。
在每天的生活环境活动识别还没有被广泛研究。大多数智能电话为基础的活动识别系统以受控方式进行评估，导致评估协议可能是有利的算法，而不是现实的真实世界环境。在他们的评估方案，参加者往往只执行用于预测的行动，而不是将大范围的现实活动的参与者进行连续，模仿现实生活中的事件。
一些智能手机HAR研究8,9组类似的活动结合起来，如楼梯，步行，但是从数据集中排除其他活动。预测精度，然后由得怎么样算法确定的目标的活动来决定。德恩巴赫等 9名学员写他们即将搬家之前执行，中断状态变化的连续转换活动。 HAR系统的评估应该评估的算法，而参与者执行在日常生活环境的自然行为。这将使现实生活中的评价，认为复制日常使用的应用程序。一个现实的电路包括许多变化，对国家以及对系统不能预见的动作组合。那么一个研究者可以评估算法应对这些额外的动作，从而评估该算法的鲁棒性异常动作。
本文提出一种使用受控的课程，反映现实生活的日常生活环境中可穿戴式移动监控系统（WMMS）评估协议。 WMMS评价就可以在控制，但现实条件进行。在这个协议中，我们使用了在渥太华大学和渥太华医院研究所11-15开发出了第三代WMMS。该WMMS是专为智能手机与一个三轴加速度计和陀螺仪。在流动性算法占用户的变化，提供了误报的数量变化，状态识别的减少，并在活动分类提高灵敏度。最大限度地减少误报很重要，因为WMMS触发时被检测到的状态的活性变化短片拍摄，上下文敏感的活性评价进一步提高WMMS分类。不必要的录像创建存储和电池的使用效率低下。该WMMS算法是使用不同的预测的水平，其中增加的预测水平意味着增加量构造为低计算学习模式并评价可识别动作。
Protocol
Representative Results
Discussion
用可穿戴式移动监控系统，人类活动的认可已收到更多的关注，近年来，由于在可穿戴计算和智能手机和定量结果的措施，帮助临床决策和卫生干预措施评价系统需求的技术进步。本文介绍的方法是有效的评估WMMS发展至今，被发现活动分类错误不会一直存在，如果没有被列入评估范围广泛的日常活动和散步的情景。
该WMMS评价协议包括两个主要部分：数据采集与伴随的黄…
開示
The authors have nothing to disclose.
Acknowledgements
作者承认埃文拜斯海姆，妮可·卡佩拉，安德鲁·赫伯特 – 科普利技术和数据收集方面的援助。项目资金由加拿大自然科学和（NSERC）工程研究理事会和黑莓公司，包括在研究中使用的智能手机接收。
Materials
| Smartphone or wearable measurement device | Blackberry | Z10 | |
| Smartphone for video recording | Blackberry | Z10 or 9800 | |
| Phone holster | Any | ||
| Data logger application for the smartphone | BlackBerry World – TOHRC Data Logger for BlackBerry 10 | http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA | |
| Wearable mobility measurement | Custom Blackberry 10 and Matlab software for mobility monitoring | http://www.irrd.ca/cag/smartphone/ | |
| Video editing or analysis software | Motion Analysis Tools | http://www.irrd.ca/cag/mat/ |
参考文献
- Lara, O. D., Labrador, M. A. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys Tutorials. 15 (3), 1192-1209 (2013).
- Foerster, F., Smeja, M., Fahrenberg, J. Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring. Computers in Human Behavior. 15 (5), 571-583 (1999).
- Elsmore, T. F., Naitoh, P. . Monitoring Activity With a Wrist-Worn Actigraph: Effects of Amplifier Passband and threshold Variations. , (1993).
- Kripke, D. F., Webster, J. B., Mullaney, D. J., Messin, S., Mason, W. . Measuring sleep by wrist actigraph. , (1981).
- Lockhart, J. W., Pulickal, T., Weiss, G. M. Applications of mobile activity recognition. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. , 1054-1058 (2012).
- Incel, O. D., Kose, M., Ersoy, C. A Review and Taxonomy of Activity Recognition on Mobile Phones. BioNanoScience. 3 (2), 145-171 (2013).
- Yang, C. C., Hsu, Y. L. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring. Sensors. 10 (8), 7772-7788 (2010).
- He, Y., Li, Y. Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. International Journal of Distributed Sensor Networks. 2013, (2013).
- Vo, Q. V., Hoang, M. T., Choi, D. Personalization in Mobile Activity Recognition System using K-medoids Clustering Algorithm. International Journal of Distributed Sensor Networks. 2013, (2013).
- Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., Cook, D. J. Simple and Complex Activity Recognition through Smart Phones. , 214-221 (2012).
- Hache, G., Lemaire, E. D., Baddour, N. Mobility change-of-state detection using a smartphone-based approach. IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA). , 43-46 (2010).
- Wu, H. H., Lemaire, E. D., Baddour, N. Change-of-state determination to recognize mobility activities using a BlackBerry smartphone. , 5252-5255 (2011).
- Wu, H., Lemaire, E. D., Baddour, N. Activity Change-of-state Identification Using a Blackberry Smartphone. Journal of Medical and Biological Engineering. 32 (4), 265-271 (2012).
- Tundo, M. D., Lemaire, E., Baddour, N. Correcting Smartphone orientation for accelerometer-based analysis. IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA). , 58-62 (2013).
- Tundo, M. D. . Development of a human activity recognition system using inertial measurement unit sensors on a smartphone. , (2014).
