This page contains the source code and data used in our paper Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art. In this paper, we implement and evaluate several state-of-the-art approaches, ranging from methods based on handcrafted features to convolutional neural networks. Also, we standardize a large number of datasets, which vary in terms of sampling rate, number of sensors, activities, and subjects. We implement and evaluate several state-of-the-art methods, summarized in the table below.
Mean accuracy achieved by the methods using the Leave-One-Subject-Out (LOSO) as validation protocol. The symbol ‘x’ means that it was not possible to execute the method on the respective dataset.
|Kwapisz et al.||90.41||71.27||70.15||13.04||66.67||42.19||75.31|
|Catal et al.||94.66||85.25||75.89||32.45||74.67||46.84||74.96|
|Kim et al.||93.90||81.57||64.20||38.05||64.60||51.48||50.22|
|Chen and Xue||88.67||83.06||75.58||x||x||61.94||83.89|
|Jiang and Yin||51.46||x||74.88||x||x||65.35||79.97|
|Ha et al.||88.34||73.79||x||x||x||x||x|
|Ha and Choi||84.23||74.21||x||x||x||x||x|
You should cite the following paper if you use this software in your work.