Hand Exoskeleton Design and Human-Machine Interaction Strategies for Rehabilitation

Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients' clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide an overview of the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The 201 selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

Improving manual dexterity using ergonomic wearable glove in patients with multiple sclerosis: A quasi-randomized clinical trial

One main problem faced by people with multiple sclerosis (PwMS) is upper limb dysfunction, which can occur in the first decade of the disease and with the highest prevalence of disability in the progressive type of the disease. Then, PwMS may benefit from personalised and intensive treatment as provided by robotic devices. These innovative devices have increasingly been brought into the neurorehabilitation field, due to their ability to provide repetitive and task-oriented training. In this quasi-randomized study, we aim to evaluate the effects of robotic-assisted hand training, using the Hand TutorTM device, on hand functionality, active RoM, and manual dexterity, compared to conventional rehabilitation in PwMS. We enrolled 30 MS patients, who received 20 training sessions, each lasting 45 min with robotic-assisted hand training with Hand Tutor (n 15, experimental group) or conventional rehabilitation therapy (n 15, control group). All patients were evaluated at pre- and post-intervention with clinical scales for upper limb functionality (DASH, BBT, NHPT, and MI). In addition, only patients in the experimental group received an objective kinematic analysis of the hand and wrist movements, delivered by the Hand Tutor glove, both pre- and post-intervention. We found that PwMS in both groups statistically improved their upper limb functions, however the experimental group achieved better results in terms of manual dexterity. This promising rehabilitation training with Hand Tutor glove led to positive effects on upper limbs motor outcomes and kinematic parameters in patients with MS.

The Design and Adaptive Control of a Parallel Chambered Pneumatic Muscle-Driven Soft Hand Robot for Grasping Rehabilitation

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability.

Breaking the ice through an effective translationality in neurorehabilitation: are we heading to the right direction?

INTRODUCTION

Translational medicine has been facing a persistent crisis for decades, and the field of neurorehabilitation is no exception. The challenges and delays that prevent patients, caregivers, and clinicians from promptly benefiting from advancements in bioengineering and new technological discoveries are well-documented.

AREAS-COVERED

This perspective presents some ideas to underline the consolidated problems and highlight new obstacles to overcome in the context of translational neurorehabilitation, also considering the increasingly stringent laws for medical devices that are emerging throughout the world.

EXPERT OPINION

The objective of the entire medical-scientific community must be to ensure that patients and their loved ones receive the best care available with the most advanced systems. Bioengineers, healthcare policy makers, certifiers and clinicians must always take translational aspects into consideration and find solutions to mitigate possible problems and delays. The goal of the entire medical and scientific community should be to ensure that patients and their families receive the highest quality care through the most advanced systems. To achieve this, bioengineers, healthcare policymakers, certifiers, and clinicians must consistently address translational challenges and work collaboratively to find solutions that minimize potential problems and delays.

Recent Advances in Wearable Healthcare Devices: From Material to Application

In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies

Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decision-making and treatment outcomes. Nevertheless, it is still a rather innovative field of application of these techniques and there is a need to find what aspects are highly developed and what possible job niches can be exploited. This systematic review aims to evaluate the current state of research on the use of a particular AI called deep learning models in physiotherapy and identify the key trends, challenges, and opportunities in this field. The findings of this review, conducted following the PRISMA guidelines, provide valuable insights for researchers and clinicians. The most relevant databases included were PubMed, Web of Science, Scopus, Astrophysics Data System, and Central Citation Export. Inclusion and exclusion criteria were established to determine which items would be considered for further review. These criteria were used to screen the items during the first and second peer review processes. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, of the 214 initial papers, 23 studies were selected. From our analysis of the selected articles, we can draw the following conclusions: Concerning deep learning models, innovation is primarily seen in the adoption of hybrid models, with convolutional models being extensively utilized. In terms of data, it's unsurprising that body signals and images are predominantly used. However, texts and structured data present promising avenues for groundbreaking work in the field. Additionally, medical tests that involve the collection of 3D images, Functional Movement Screening, or thermographies emerge as novel areas to explore applications within the scope of physiotherapy.

Advances in Biomechanics-Based Motion Analysis

Motion patterns in humans have been closely associated with neurological/musculoskeletal/behavioral/psychological health issues and competitive sports performance [...].

SenGlove—A Modular Wearable Device to Measure Kinematic Parameters of The Human Hand

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability of handling and manipulating objects and communicating with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy, as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process, as well as commercially available components, are used to develop a sensorized glove. The proposed wearable introduced in this paper can measure 14 of 15 angular values of a simplified hand model. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with six degrees of freedom are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable to a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles on one finger, surpassing the human perception threshold and the current state-of-the-art in science and technology for comparable systems.