SisFall: A Fall and Movement Dataset

By Angela Sucerquia, J. López, Jesús Francisco Vargas-Bonilla. Italian National Conference on Sensors, 2017

468 citations recorded by the source corpus.

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/c45c8cdc61bc294e6650b7d35fb68686cedfce2e

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Citations in the corpus

• UP-Fall Detection Dataset: A Multimodal Approach (2019, background): “SisFall is a dataset [15] of falls and ADL obtained with self-developed embedded device with two accelerometers and one gyroscope.”
• UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, background): “This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [32], Ugulino [33], USC HAD [34], DaLiAc [10], EvAAL [35], MHEALTH [36], UCI ARSA [27], BaSA [37], UR Fall Detection [38], MMsys [9], SisFall [39], UMA Fall 4 [18], and REALDISP [40].”