Learning personalized ADL recognition models from few raw data

Abstract

Recognition of Activities of Daily Living (ADL) is an essential component of assisted living systems based on actigraphy. This task can nowadays be performed by machine learning models which are able to automatically extract and learn relevant features but, most of time, need to be trained with large amounts of data collected on several users. In this paper, we propose an approach to learn personalized ADL recognition models from few raw data based on a specific type of neural network called matching network. The interest of this few-shot learning approach is three-fold. Firstly, people perform activities their own way and general models may average out important individual characteristics unlike personalized models that could thus achieve better performance. Secondly, gathering large quantities of annotated data from one user is time-consuming and threatens privacy in a medical context. Thirdly, matching networks are by nature weakly dependent on the classes they are trained on and can generalize easily to new activities without needing extra training, thus making them very versatile for real applications. Our results show the effectiveness of the proposed approach compared to general neural network models, even in situations with few training data.

Machine Learning Based Fall Detector With a Sensorized Tip

“…Among these, multiple solutions can be found using accelerometers in the literature [19], [28]- [39]. Some works also propose the use of IMUs (Inertial Measurement Unit) which combine the former with gyroscopes and magnetometers and allow to estimate the 3D orientation of the device in a global reference system [40]- [43]. A particular subset of these approaches use the internal IMUs of current smartphones [44]- [47].…”

A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip

“…Finally, in the third step, using the set of selected features, the PA identification or classification is performed. Machine Learning (ML) techniques such as K-Nearest Neighbour (K-NN) [10], [24], [28], Support Vector Machine (SVM) [29]- [31] and Artificial Neural Networks (ANN) [15], [32]- [34] are the preferred solution for gait-related PA classification due to their flexibility and capability of generalization, which provide acceptable results with a success rate up to 91% [15]. Note that all these approaches are of supervised nature, and require a set of properly designed training data in which the selected features are the input, and the type of PA to be identified are the outputs.…”