Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning

Parkinson's disease (PD) is characterised by a progressive worsening of motor functionalities. In particular, limited hand dexterity strongly correlates with PD diagnosis and staging. Objective detection of alterations in hand motor skills would allow, for example, prompt identification of the disease, its symptoms and the definition of adequate medical treatments. Among the clinical assessment tasks to diagnose and stage PD from hand impairment, the Finger Tapping (FT) task is a well-established tool. This preliminary study exploits a single RGB-Depth camera (Azure Kinect) and Google MediaPipe Hands to track and assess the Finger Tapping task. The system includes several stages. First, hand movements are tracked from FT video recordings and used to extract a series of clinically-relevant features. Then, the most significant features are selected and used to train and test several Machine Learning (ML) models, to distinguish subjects with PD from healthy controls. To test the proposed system, 35 PD subjects and 60 healthy volunteers were recruited. The best-performing ML model achieved a 94.4% Accuracy and 98.4% Fl score in a Leave-One-Subject-Out validation. Moreover, different clusters with respect to spatial and temporal variability in the FT trials among PD subjects were identified. This result suggests the possibility of exploiting the proposed system to perform an even finer identification of subgroups among the PD population

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson's Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient's needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user's engagement during an exergame. As a first step, healthy subjects were recruited to assess the system's capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system

Kinect-based Solution for the Home Monitoring of Gait and Balance in Elderly People with and without Neurological Diseases

Alterations of gait and balance are a significant cause of falls, injuries, and consequent hospitalizations in the elderly. In addition to age-associated motor decline, other factors can impact gait and stability, including the motor dysfunctions caused by neurological diseases such as Parkinson’s disease or hemiplegia after stroke. Monitoring changes and deterioration in gait patterns and balance is crucial for activating rehabilitation treatments and preventing serious consequences. This work presents a Kinect-based solution, suitable for domestic contexts, for assessing gait and balance in individuals at risk of falling. The system captures body movements during home acquisition sessions scheduled by clinicians at definite times of the day and automatically estimates specific functional parameters to objectively characterize the subjects’ performance. The system includes a graphical user interface designed to ensure usability in unsupervised contexts: the human-computer interaction mainly relies on natural body movements to support the self-management of the system, if the motor conditions allow it. This work presents the system’s features and facilities, and the preliminary results on healthy volunteers’ trials

A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects

Parkinson's disease (PD) severity is assessed through a set of standardised tasks defined by clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). In particular, Leg Agility is a well-established test among the motor tasks included in UPDRS, which consists in repeated cycles of knee lifting and lowering, while sitting on a chair. Leg Agility objective evaluation through optical devices is often investigated for telemedicine applications. Moreover, remote rehabilitation for PD subjects through virtual exergaming is becoming a popular approach thanks to its versatility, increased user engagement and the possibility of coupling it with remote monitoring tools. This work investigates if lower-limb exergaming may also be exploited for assessment purposes similar to traditional evaluation. In particular, if there exists a statistical difference between the kinematic description of Leg Agility versus the one of a Bouncing Ball exergame, as provided by an optical (RGB-D) acquisition system suitable for remote monitoring. Preliminary results obtained by the comparison of the two types of assessment in a small group of parkinsonian subjects are presented and discussed

Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications

Electrodermal Activity (EDA) is a broadly-investigated physiological signal, whose behaviour is connected to nervous system arousal. Such system, indeed, influences the properties of the skin, producing a measurable electrical signal. Among the possible applications of such measurements, several studies have correlated the signal behaviour to engagement during mental and physical tasks, and the subjects' response to specific multimodal stimuli. Also due to the possibility of performing remote assessment and rehabilitation, telemedicine applications are gaining ground in the healthcare system. However, acceptance and engagement, hence continuity of usage, still remain significant obstacles. Therefore, it would be highly beneficial to verify, through objective measures, if these solutions are actually providing a sufficient stimulation to properly engage subjects while playing. This study investigates the possibility of employing EDA in the automatic recognition of different levels of user engagement, while playing a motor-cognitive exergame specifically designed for this purpose. Preliminary results, obtained on a cohort of 25 healthy subjects, seem to confirm that features extracted from EDA analysis are significant and able to train supervised classifiers, achieving high accuracy and precision in the engagement recognition problem

GMH-D: Combining Google MediaPipe and RGB-Depth Cameras for Hand Motor Skills Remote Assessment

Impairment in the execution of simple motor tasks involving hands and fingers could hint at a general worsening of health conditions, particularly in the elderly and in people affected by neurological diseases. The deterioration of hand motor function strongly impacts autonomy in daily activities and, consequently, the perceived quality of life. The early detection of alterations in hand motor skills would allow, for example, to promptly activate treatments and mitigate this discomfort. This preliminary study examines an innovative pipeline based on a single RGB-Depth camera and Google MediaPipe Hands, that is suitable for the remote assessment of hand motor skills through simple tasks commonly used in clinical practice. The study includes several phases. First, the quality of hand tracking is evaluated by comparing reconstructed and real hand 3D trajectories. The proposed solution is then tested on a cohort of healthy volunteers to estimate specific kinematic features for each task. Finally, these features are used to train supervised classifiers and distinguish between “normal” and “altered” performance by simulating typical motor behaviour of real impaired subjects. The preliminary results show the ability of the proposed solution to automatically highlight alterations in hand performance, providing an easy-to-use and non-invasive tool suitable for remote monitoring of hand motor skills

Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks

Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human-computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however, their performance in accurately and reliably measuring fingers movements requires validation against well-established gold standard systems. In this paper, the aim is to validate the handtracking framework implemented by Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that exploits the depth estimation of an RGB-Depth camera to achieve more accurate tracking of 3D movements. Three dynamic exercises commonly administered by clinicians to assess hand dysfunctions, namely Hand Opening-Closing, Single Finger Tapping and Multiple Finger Tapping are considered. Results demonstrate high temporal and spectral consistency of both frameworks with the gold standard. However, the enhanced GMH-D framework exhibits superior accuracy in spatial measurements compared to the baseline GMH, for both slow and fast movements. Overall, our study contributes to the advancement of hand tracking technology, the establishment of a validation procedure as a good-practice to prove efficacy of deep-learning-based hand-tracking, and proves the effectiveness of GMH-D as a reliable framework for assessing 3D hand movements in clinical applications

Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitabilit

Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson’s Disease Using the Azure Kinect Sensor

Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, neurological diseases, hemiplegia, and other comorbidities that affect motor control and coordination. Objective assessment of arm swings during walking could play a role in preventing adverse consequences, allowing appropriate treatments and rehabilitation protocols to be activated for recovery and improvement. This paper presents a system for gait analysis based on Microsoft Azure Kinect DK sensor and its body-tracking algorithm: It allows noninvasive full-body tracking, thus enabling simultaneous analysis of different aspects of walking, including arm swing characteristics. Sixteen subjects with Parkinson’s disease and 13 healthy controls were recruited with the aim of evaluating differences in arm swing features and correlating them with traditional gait parameters. Preliminary results show significant differences between the two groups and a strong correlation between the parameters. The study thus highlights the ability of the proposed system to quantify arm swing features, thus offering a simple tool to provide a more comprehensive gait assessment

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement- based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies