AnkFall-Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 5; p. 1889
• Main Authors: Luna-Perejón, Francisco, Muñoz-Saavedra, Luis, Civit-Masot, Javier, Civit, Anton, Domínguez-Morales, Manuel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.03.2021; MDPI
• Subjects: accelerometer; Accelerometers; Accelerometry; Accidental Falls - prevention & control; Activities of Daily Living; Aged; Algorithms; Ankle; COVID-19; Datasets; deep learning; embedded system; Fall detection; Falling; Humans; Neural networks; Neural Networks, Computer; Older people; Recurrent neural networks; Remote monitoring; Sensors; Wear; wearable; Wearable Electronic Devices; Wrist; embedded system; deep learning; wearable; recurrent neural networks; fall detection; accelerometer
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21051889

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system's feasibility.