Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions
Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions
复制标题
DOI：
10.1109/titb.2007.899496
复制
发表时间：
2008-01-01
影响因子：
--
通讯作者：
Korhonen, Ilkka
中科院分区：
文献类型：
--
作者：
Ermes, Miikka;Parkka, Juha;Korhonen, Ilkka
体育活动对人们的幸福感有积极的影响，也可能减少慢性病的发生。使用可穿戴传感器的活动识别可以向用户提供关于他/她的关于身体活动和运动的生活方式的反馈，从而促进更积极的生活方式。到目前为止，活动识别主要是在有监督的实验室环境中研究的。本研究的目的是研究如何以及日常活动和运动进行的主题在无监督的设置可以识别相比，监督设置。的活动被确认，通过使用一个混合分类器相结合的树结构，包含先验知识和人工神经网络，也通过使用三个参考分类。收集12名受试者68 h的活动数据，其中21 h的活动受到监督，47 h的活动没有受到监督。活动是根据来自臀部和手腕上的3-D加速度计和GPS信息的信号特征来识别的。这些活动包括躺下，坐着和站着，步行，跑步，骑自行车，划船机划船，踢足球，北欧式步行和骑普通自行车。使用监督和非监督数据的活动识别的总准确度为89%，仅比仅使用监督数据的活动识别的准确度低1%单位。然而，当仅使用监督数据进行训练和仅使用非监督数据进行验证时，准确性降低了17%，这强调了在开发活动识别系统时需要实验室外数据。这些结果支持了在真实的生活环境中识别更广泛、更复杂的活动的愿景。
Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89 % that was only 1 % unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.