Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible use and better allocation of resources; especially in the medical industry. Activity recognition and classification can be obtained using many sophisticated data recording setups, but there is also a need in observing how performance varies among models that are strictly limited to using sensor data from easily accessible devices: smartphones and smartwatches. This paper presents the findings of different models that are limited to train using such sensors. The models are trained using either the k-Nearest Neighbor, Support Vector Machine, or Random Forest classifier algorithms. Performance and evaluations are done by comparing various model performances using different combinations of mobile sensors and how they affect recognitive performances of models. Results show promise for models trained strictly using limited sensor data collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.

翻译：人类活动的认识随着日常生活方式和医疗环境中应用的增多而日益受到欢迎。高效和可靠的人类活动认识带来了各种好处,例如便于使用和更好地分配资源,特别是在医疗行业。活动确认和分类可以使用许多复杂的数据记录装置获得,但也需要观察严格限于使用容易获取的装置(智能手机和智能观察)的感应数据的各种模型的性能如何不同。本文介绍了限于培训使用这种传感器的不同模型的研究结果。模型是使用K-Nearest Neighbor、支持矢量机器或随机森林分类算法来培训的。通过比较各种模型性能,使用不同的移动感应器组合,以及它们如何影响模型的感应性能,来进行业绩和评估。结果显示了严格使用从智能手机和智能观察中收集的有限感应数据以及传统机器学习概念和算法来培训模型的前景。