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.
Results
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.
Method | MHEALTH | PAMAP2 | USCHAD | UTD-1 | UTD-2 | WHARF | WISDM |
---|---|---|---|---|---|---|---|
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 |
References
You should cite the following paper if you use this software in your work.
Human Activity Recognition based on Wearable Sensor Data: A Benchmark. In: arXiv, pp. 1-12, 2018. | :