An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body

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.

Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

A review of cable-driven rehabilitation devices

Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed.Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.Implications for rehabilitationCDRDs (and general RRDs) can deliver high-intensity training while therapists usually cannot. With up-to-date human-robot interaction techniques (e.g., virtual reality), CDRDs are more interesting and motivating to trainees than conventional manual rehabilitation therapies. CDRDs also provide financial benefits in the long-run. Currently existing RRDs available for clinical practice are mainly designed for the rehabilitation of shoulders, elbows, and knees. Parallel exoskeleton-based CDRDs can also be used for the rehabilitation of many other parts of trainees. Thus, CDRDs extend the coverage of RRDs in rehabilitation. Owing to their simple structures and light weights, CDRDs can be portable and used for rehabilitation at home. In this way, CDRDs can improve the duration and intensity of rehabilitation for those with limited access to rehabilitation institutes. As well known, the higher intensity of training leads to a higher rate of recovery.

Robust and Accurate Capture of Human Joint Pose Using an Inertial Sensor

Wearable inertial measurement units (IMU) measuring acceleration, earth magnetic field, and gyroscopic measurements can be considered for capturing human skeletal postures in real time. Number of movement disorders require accurate and robust estimation of the human joint pose. Though these movements are inherently slow, the accuracy of estimation is vital as many subtle moment patterns, such as tremor are useful to capture under many assessments scenarios. Also, as the end user is a patient with movement disabilities, the practical wearability aspects impose stringent requirements such as the use of minimal number of sensors as well as positioning them in conformable areas of the human body; particularly for longer term monitoring. Estimating skeletal and limb orientations to describe human posture dynamically via model-based approaches poses numerous challenges. In this paper, we convey that the use of measurement conversion ideas-a representation signifying a linear characterization of an inherently non linear estimation problem, pragmatically improves the overall estimation of the limb orientation. A quaternion, as opposed to the Euler angle-based approach is adopted to avoid Gimbal lock scenarios. We also lay a systematic basis for quaternion normalization, typically performed in the pre-filtering stage, by introducing an optimization-based mathematical justification. A robust version of the extended Kalman filter is configured to amalgamate the underlying ideas in enhancing the overall system performance while providing a structured and a comprehensive approach to IMU-based real time human pose estimation problem, particularly in a movement disability capture context.

Mobile Mechatronic/Robotic Orthotic Devices to Assist-Rehabilitate Neuromotor Impairments in the Upper Limb: A Systematic and Synthetic Review

This paper overviews the state-of-the-art in upper limb robot-supported approaches, focusing on advancements in the related mechatronic devices for the patients' rehabilitation and/or assistance. Dedicated to the technical, comprehensively methodological and global effectiveness and improvement in this inter-disciplinary field of research, it includes information beyond the therapy administrated in clinical settings-but with no diminished safety requirements. Our systematic review, based on PRISMA guidelines, searched articles published between January 2001 and November 2017 from the following databases: Cochrane, Medline/PubMed, PMC, Elsevier, PEDro, and ISI Web of Knowledge/Science. Then we have applied a new innovative PEDro-inspired technique to classify the relevant articles. The article focuses on the main indications, current technologies, categories of intervention and outcome assessment modalities. It includes also, in tabular form, the main characteristics of the most relevant mobile (wearable and/or portable) mechatronic/robotic orthoses/exoskeletons prototype devices used to assist-rehabilitate neuromotor impairments in the upper limb.

Wearable Improved Vision System for Color Vision Deficiency Correction

Color vision deficiency (CVD) is an extremely frequent vision impairment that compromises the ability to recognize colors. In order to improve color vision in a subject with CVD, we designed and developed a wearable improved vision system based on an augmented reality device. The system was validated in a clinical pilot study on 24 subjects with CVD (18 males and 6 females, aged 37.4 ± 14.2 years). The primary outcome was the improvement in the Ishihara Vision Test score with the correction proposed by our system. The Ishihara test score significantly improved ([Formula: see text]) from 5.8 ± 3.0 without correction to 14.8 ± 5.0 with correction. Almost all patients showed an improvement in color vision, as shown by the increased test scores. Moreover, with our system, 12 subjects (50%) passed the vision color test as normal vision subjects. The development and preliminary validation of the proposed platform confirm that a wearable augmented-reality device could be an effective aid to improve color vision in subjects with CVD.

Android Platform for Realtime Gait Tracking Using Inertial Measurement Units

One of the most important movements performed by the humans is gait. Biomechanical Gait analysis is usually by optical capture systems. However, such systems are expensive and sensitive to light and obstacles. In order to reduce those costs a system based on Inertial Measurements Units (IMU) is proposed. IMU are a good option to make movement analisys indoor with a low post-processing data, allowing to connect those systems to an Android platform. The design is based on two elements: a) The IMU sensors and the b) Android device. The IMU sensor is simple, small (35 x 35 mm), portable and autonomous (7.8 hrs). A resolution of 0.01° in their measurements is obtained, and sends data via Bluetooth link. The Android application works for Android 4.2 or higher, and it is compatible with Bluetooth devices 2.0 or higher. Three IMU sensors send data to a Tablet wirelessly, in order to evaluate the angles evolution for each joint of the leg (hip, knee and ankle). This information is used to calculate gait index and evaluate the gait quality online during the physical therapist is working with the patient.