On-body localization of wearable devices: An investigation of position-aware activity recognition

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Flower: A Friendly Federated Learning Research Framework (2020, methodology): “We used the RealWorld dataset (Sztyler & Stuckenschmidt, 2016) consisting of time-series data from accelerometer and gyroscope sensors on mobile devices, and partitioned it across 10 mobile clients.”
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “A segmentation operation is necessary before applying any cla sification method [8].”
Sensor-based and vision-based human activity recognition: A comprehensive survey (2020, background): “On the other hand, benchmark datasets are often used by the researchers to verify the proposed frameworks [98,99] .”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, neutral): “This section describes the method used to acquire and pre-process samples in order to produce the UniMiB SHAR dataset.”
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation (2018, methodology): “We observe that HDCNN achieves ≈ 76% and ≈ 73 %classiﬁcation accuracy, for HHAR and HAR respectively, when adapting across device-instances.”