Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Neuromechanical Biomarkers for Robotic Neurorehabilitation

: One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the "biomarkers" that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the "Rehabilomics" has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective

Real-time classification of finger movements using two-channel surface electromyography

The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively

Experimental protocol to investigate cortical, muscular and body representation alterations in adolescents with idiopathic scoliosis

Background: Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis. AIS is a three-dimensional morphological spinal deformity that affects approximately 1-3% of adolescents. Not all factors related to the etiology of AIS have yet been identified. Objective: The primary aim of this experimental protocol is to quantitatively investigate alterations in body representation in AIS, and to quantitatively and objectively track the changes in body sensorimotor representation due to treatment. Methods: Adolescent girls with a confirmed diagnosis of mild (Cobb angle: 10°-20°) or moderate (21°-35°) scoliosis as well as age and sex-matched controls will be recruited. Participants will be asked to perform a 6-min upright standing and two tasks-named target reaching and forearm bisection task. Eventually, subjects will fill in a self-report questionnaire and a computer-based test to assess body image. This evaluation will be repeated after 6 and 12 months of treatment (i.e., partial or full-time brace and physiotherapy corrective postural exercises). Results: We expect that theta brain rhythm in the central brain areas, alpha brain rhythm lateralization and body representation will change over time depending on treatment and scoliosis progression as a compensatory strategy to overcome a sensorimotor dysfunction. We also expect asymmetric activation of the trunk muscle during reaching tasks and decreased postural stability in AIS. Conclusions: Quantitatively assess the body representation at different time points during AIS treatment may provide new insights on the pathophysiology and etiology of scoliosis

Longitudinal tracking of physiological state with electromyographic signals.

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A LASSO regularization is performed to observe changes in relationship between electromyography features and force plate outcomes. SVM classifiers are employed to correctly identify the times at which these experiments are performed, which is important as these indicate a trajectory of adaptation

BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients

This presentation summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset 'OpenBCI' and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating

Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography

Background: Several regression models have been proposed for estimation of isometric joint torque using surfaceelectromyography (SEMG) signals. Common issues related to torque estimation models are degradation of modelaccuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares theperformance of the most commonly used regression models under these circumstances, in order to assistresearchers with identifying the most appropriate model for a specific biomedical application.Methods: Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor,was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eightforearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data weregathered one hour and twenty-four hours following the completion of the first data gathering session, for thepurpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. AcquiredSEMG signals were filtered, rectified, normalized and then fed to models for training.Results: It was shown that mean adjusted coefficient of determination (R2a) values decrease between 20%-35% fordifferent models after one hour while altering arm posture decreased mean R2avalues between 64% to 74% fordifferent models.Conclusions: Model estimation accuracy drops significantly with passage of time, electrode displacement, andalteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resamplingcan significantly reduce model training time without losing estimation accuracy. Among the models compared,ordinary least squares linear regression model (OLS) was shown to have high isometric torque estimation accuracycombined with very short training times

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare