The integration of IoT and deep learning provides the opportunity for continuous monitoring and evaluation of patients’ health status, leading to more personalized treatment and improved quality of life. This study explores the potential of deep learning to predict episodes of freezing of gait (FoG) in Parkinson’s disease (PD) patients. Initially, a literature review was conducted to determine the state of the art; then, two inception-based models, namely LN-Inception and InSEption, were introduced and tested using the Daphnet dataset and an additional novel medium-sized dataset collected from an IMU (inertial measuring unit) sensor. The results show that both models performed very well, outperforming or achieving performance comparable to the state-of-the-art. In particular, the InSEption network showed exceptional performance, achieving a 6% increase in macro F1 score compared to the inception-only-based counterpart on the Daphnet dataset. In a newly introduced IMU dataset, InSEption scored 97.2% and 98.6% in terms of F1 and AUC, respectively. This can be attributed to the added squeeze and excitation blocks and the domain-specific oversampling methods used for training. The benefits of using the Inception mechanism for signal data and its potential for integration into wearable IoT are validated.

Nowadays, neurological diseases represent a medical emergency for which new prevention, monitoring and adequate treatment solutions are needed. ALAMEDA project proposes a monitoring solution for patients with Parkinson’s disease, Multiple Sclerosis, and Stroke, using multiple sensors and specific applications to collect information on the condition of health and aspects of lifestyle, activity level, sociability and mood. The paper describes the ALAMEDA infrastructure architecture and  all processes to support the dedicated AI toolkit, along with all relevant concepts, components and interactions. Data collection methods are central to the objectives of the ALAMEDA project, as they are responsible for the acquisition of all data necessary for patient monitoring and evaluation during the pilot period. In this sense, the data flow for the data collection service for smart bracelets and smart insoles is presented, as well as how users interact with these devices.

Facial expression recognition (FER) is an emerging and multifaceted research topic. Applications of FER in healthcare, security, safe driving, and so forth have contributed to the credibility of these methods and their adoption in human-computer interaction for intelligent outcomes.
Computational FER mimics human facial expression coding skills and conveys important cues that complement speech to assist listeners. Similarly, FER methods based on deep learning and artificial intelligence (AI) techniques have been developed with edge modules to ensure efficiency and real-time processing. To this end, numerous studies have explored different aspects of FER.

Sleep is a key aspect affecting health, cognitive functionality, and human psychology on all occasions. There- fore, on the one hand, sleep greatly impacts the quality of life, while on the other hand poor health and/or psychology often deteriorate the quality of sleep. Moving beyond the golden standard for sleep studies, i.e. polysomnography, and building on the current state of the art in wearables, this paper aims to propose a deep learning approach that focuses on sleep stage classification, introducing the timeseries related information input to the classification. In this respect, smartwatch sensor measurements are used and a series of meth- ods have been tested. The proposed approach constitutes a preliminary work on sleep stage classification introducing a novel approach of feature engineering incorporating the time-related information concerning the transition of the sleep stages via a Long Short-Term Memory (LSTM) encoding of the accelerometer data from smartwaches.

Parkinson’s disease (PD) is a progressive neurodegenerative disease with motor and non-motor symptoms characterized by complex interactions between genetic and environmental factors. These factors influence the onset and varying evolution rate of PD, however the exact mechanisms remain unclear. Given the ineffective symptomatic treatments of PD, the research priority is focused on prevention and the recognition of modifiable environmental risk factors including pesticides, heavy metals, viruses, and air pollution.

This paper presents Zenon, an affective, multi-modal conversational agent (chatbot) specifically designed for treatment of brain diseases like multiple sclerosis and stroke.

Zenon collects information from patients in a non-intrusive way and records user sentiment using two different modalities: text and video. A user-friendly interface is designed to meet users’ needs and achieve an efficient conversation flow. What makes Zenon unique is the support of multiple languages, the combination of two information sources for tracking sentiment, and the deployment of a semantic knowledge graph that ensures machine-interpretable information exchange.