Benchmark on Activity Recognition based on Wearable Sensors

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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.

MethodMHEALTHPAMAP2USCHADUTD-1UTD-2WHARFWISDM
Kwapisz et al.90.4171.2770.1513.0466.6742.1975.31
Catal et al.94.6685.2575.8932.4574.6746.8474.96
Kim et al.93.9081.5764.2038.0564.6051.4850.22
Chen and Xue88.6783.0675.58xx61.9483.89
Jiang and Yin51.46x74.88xx65.3579.97
Ha et al.88.3473.79xxxxx
Ha and Choi84.2374.21xxxxx

References

You should cite the following paper if you use this software in your work.

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Artur Jordao; Antonio Carlos Nazare Junior; Jessica Sena; William Robson Schwartz: Human Activity Recognition based on Wearable Sensor Data: A Benchmark. In: arXiv, pp. 1-12, 2018. (Type: Journal Article | Links | BibTeX)

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