CNN for Elderly Wandering Prediction in Indoor Scenarios

Abstract

This work proposes a way to detect the wandering movement of Alzheimer’s patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

“…The lack of proper data induced Oliveira et al [ 126 ] to fabricate the wandering and normal movement paths required for their research, which simulate the real world. In a future work these authors will validate their system with data collected from real patients.…”

“…Deep neural network approaches. As with shallow ANNs, private datasets were the most frequent when gait data was required [124][125][126]. In the first work, built-in sensors of cell phones were utilized to obtain the gait data whereas different in-home sensors were preferred in the latter two works.…”

A Wandering Detection Method Based on Processing GPS Trajectories Using the Wavelet Packet Decomposition Transform for People with Cognitive Impairment

“…This ensures that the algorithm's performance remains unaffected when traversing any new path. Numerous wandering detection algorithms developed in previous studies [17,19,[25][26][27]29,[32][33][34][35][37][38][39][40][41][42], including machine-learning-based methods and next location prediction algorithms, rely on the patient's historical motion behaviors. However, there exists a potential disruption in the algorithm's performance when confronted with new behaviors that contradict the historical patterns.…”

“…Furthermore, in [39], two time series processing techniques, the autocorrelation function and the partial autocorrelation function, used in conjunction with machine learning algorithms, were used to classify wandering patterns. Other studies from this group involve proposing techniques for wandering detection based on an LSTM-based deep classification method using off-the-shelf Wi-Fi devices [40], determining frequent locations between which movements occur by transforming GPS data into geohash sequences [41], and integrating a convolutional neural network (CNN) into the IoT architecture [42].…”