Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.

As the population ages, health issues like injurious falls demand more attention. One solution is to use wearable devices to detect falls. Nevertheless, most of these devices raise obtrusiveness, and older people generally resist or might forget to wear them. The millimeter-wave (mmWave) radar technology was used in this study to unobtrusively detect human falls. Data were collected from healthy young volunteers with the radar mounted on the side wall (trial 1) or overhead (trial 2) of an experimental room. A set of features was manually extracted from the data point clouds, then multilayer perceptron (MLP), Random Forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers were applied on the features. Additionally, we devised a convolutional neural network (CNN)-based deep learning model for the underlying fall detection problem that receives a 3D representation of the point cloud data, known as occupancy grid, as the input. The optimal installation position of the radar sensor was unknown. Therefore, the sensor was mounted on side wall, and on the ceiling of the room to allow the performance comparison between these sensor placements. RF classifier achieved the best results in trial 2 (accuracy of 92.2%, recall of 0.881, precision of 0.805, and F1-score of 0.841), the proposed CNN model achieved slightly better results comparing to the RF method in trial 2 (accuracy of 92.3%, recall of 0.891, precision of 0.801, and F1-score of 0.844). These results suggest the development of an unobtrusive monitoring system for fall detection using mmWave radar is feasible.

Routing in intermittently connected mobile ad hoc networks (ICMAN) is a very challenging problem because disconnections are prevalent and the lack of knowledge about network dynamics hinders good decision making. In ICMAN one of the most important decisions is how to choose the most suitable intermediate(s) to forward the message to the destination. We propose in this document a new algorithm (PROPICMAN) that, based on context information, allows the sender to select the neighbor(s) such that the message has the highest probability to reach the destination.

Bipolar Disorder is a severe form of mental illness. It is characterized by alternated episodes of mania and depression, and it is treated typically with a combination of pharmacotherapy and psychotherapy. Recognizing early warning signs of upcoming phases of mania or depression would be of great help for a personalized medical treatment. Unfortunately, this is a difficult task to be performed for both patient and doctors. In this paper we present the MONARCA wearable system, which is meant for recognizing early warning signs and predict maniac or depressive episodes. The system is a smartphone-centred and minimally invasive wearable sensors network that is being developing in the framework of the MONARCA European project.

Given the aging population, healthcare systems need to be established to deal with health issues such as injurious falls. Wearable devices can be used to detect falls. However, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this study, we developed an unobtrusive monitoring system using infrared technology to unobtrusively detect locations and recognize human activities such as sitting, standing, walking, lying, and falling. We prototyped a system consisting of two 24×32 thermal array sensors and collected data from healthy young volunteers performing ten different scenarios. A supervised deep learning (DL)-based approach classified activities and detected locations from images. The performance of the DL approach was also compared with the machine learning (ML)-based methods. In addition, we fused the data of two sensors and formed a stereo system, which resulted in better performance compared to a single sensor. Furthermore, to detect critical activities such as falling and lying on floor, we performed a binary classification in which one class was falling plus lying on floor and another class was all the remaining activities. Using the DL-based algorithm on the stereo dataset to recognize activities, overall average accuracy and F1-score were achieved as 97.6%, and 0.935, respectively. These scores for location detection were 97.3%, and 0.927, respectively. These scores for binary classification were 97.9%, and 0.945, respectively. Our results suggest the proposed system recognized human activities, detected locations, and detected critical activities namely falling and lying on floor accurately.