Hand rehabilitation assessment system using leap motion controller

AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Stretchable glove for accurate and robust hand pose reconstruction based on comprehensive motion data

We propose a compact wearable glove capable of estimating both the finger bone lengths and the joint angles of the wearer with a simple stretch-based sensing mechanism. The soft sensing glove is designed to easily stretch and to be one-size-fits-all, both measuring the size of the hand and estimating the finger joint motions of the thumb, index, and middle fingers. The system was calibrated and evaluated using comprehensive hand motion data that reflect the extensive range of natural human hand motions and various anatomical structures. The data were collected with a custom motion-capture setup and transformed into the joint angles through our post-processing method. The glove system is capable of reconstructing arbitrary and even unconventional hand poses with accuracy and robustness, confirmed by evaluations on the estimation of bone lengths (mean error: 2.1 mm), joint angles (mean error: 4.16°), and fingertip positions (mean 3D error: 4.02 mm), and on overall hand pose reconstructions in various applications. The proposed glove allows us to take advantage of the dexterity of the human hand with potential applications, including but not limited to teleoperation of anthropomorphic robot hands or surgical robots, virtual and augmented reality, and collection of human motion data.

Exergames as a rehabilitation tool to enhance the upper limbs functionality and performance in chronic stroke survivors: a preliminary study

Introduction

Post-stroke hemiplegia commonly occurs in stroke survivors, negatively impacting the quality of life. Despite the benefits of initial specific post-acute treatments at the hospitals, motor functions, and physical mobility need to be constantly stimulated to avoid regression and subsequent hospitalizations for further rehabilitation treatments.

Method

This preliminary study proposes using gamified tasks in a virtual environment to stimulate and maintain upper limb mobility through a single RGB-D camera-based vision system (using Microsoft Azure Kinect DK). This solution is suitable for easy deployment and use in home environments. A cohort of 10 post-stroke subjects attended a 2-week gaming protocol consisting of Lateral Weightlifting (LWL) and Frontal Weightlifting (FWL) gamified tasks and gait as the instrumental evaluation task.

Results and discussion

Despite its short duration, there were statistically significant results (p < 0.05) between the baseline (T0) and the end of the protocol (TF) for Berg Balance Scale and Time Up-and-Go (9.8 and -12.3%, respectively). LWL and FWL showed significant results for unilateral executions: rate in FWL had an overall improvement of 38.5% (p < 0.001) and 34.9% (p < 0.01) for the paretic and non-paretic arm, respectively; similarly, rate in LWL improved by 19.9% (p < 0.05) for the paretic arm and 29.9% (p < 0.01) for non-paretic arm. Instead, bilateral executions had significant results for rate and speed: considering FWL, there was an improvement in rate with p < 0.01 (31.7% for paretic arm and 37.4% for non-paretic arm), whereas speed improved by 31.2% (p < 0.05) and 41.7% (p < 0.001) for the paretic and non-paretic arm, respectively; likewise, LWL showed improvement in rate with p < 0.001 (29.0% for paretic arm and 27.8% for non-paretic arm) and in speed with 23.6% (p < 0.05) and 23.5% (p < 0.01) for the paretic and non-paretic arms, respectively. No significant results were recorded for gait task, although an overall good improvement was detected for arm swing asymmetry (-22.6%). Hence, this study suggests the potential benefits of continuous stimulation of upper limb function through gamified exercises and performance monitoring over medium-long periods in the home environment, thus facilitating the patient's general mobility in daily activities.

A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step toward the development of wearable hand robots. Electromyography (EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition toward application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.

LEAP Motion Technology and Psychology: A Mini-Review on Hand Movements Sensing for Neurodevelopmental and Neurocognitive Disorders

Technological advancement is constantly evolving, and it is also developing in the mental health field. Various applications, often based on virtual reality, have been implemented to carry out psychological assessments and interventions, using innovative human-machine interaction systems. In this context, the LEAP Motion sensing technology has raised interest, since it allows for more natural interactions with digital contents, via an optical tracking of hand and finger movements. Recent research has considered LEAP Motion features in virtual-reality-based systems, to meet specific needs of different clinical populations, varying in age and type of disorder. The present paper carried out a systematic mini-review of the available literature using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The inclusion criteria were (i) publication date between 2013 and 2020, (ii) being an empirical study or project report, (iii) written in English or Italian languages, (iv) published in a scholarly peer-reviewed journal and/or conference proceedings, and (v) assessing LEAP Motion intervention for four specific psychological domains (i.e., autism spectrum disorder, attention-deficit/hyperactivity disorder, dementia, and mild cognitive impairment), objectively. Nineteen eligible empirical studies were included. Overall, results show that protocols for attention-deficit hyperactivity disorder and autism spectrum disorder can promote psychomotor and psychosocial rehabilitation in contexts that stimulate learning. Moreover, virtual reality and LEAP Motion seem promising for the assessment and screening of functional abilities in dementia and mild cognitive impairment. As evidence is, however, still limited, deeper investigations are needed to assess the full potential of the LEAP Motion technology, possibly extending its applications. This is relevant, considering the role that virtual reality could have in overcoming barriers to access assessment, therapies, and smart monitoring.

Visualizing and Evaluating Finger Movement Using Combined Acceleration and Contact-Force Sensors: A Proof-of-Concept Study

The 10-s grip and release is a method to evaluate hand dexterity. Current evaluations only visually determine the presence or absence of a disability, but experienced physicians may also make other diagnoses. In this study, we investigated a method for evaluating hand movement function by acquiring and analyzing fingertip data during a 10-s grip and release using a wearable sensor that can measure triaxial acceleration and strain. The subjects were two healthy females. The analysis was performed on the x-, y-, and z-axis data, and absolute acceleration and contact force of all fingertips. We calculated the variability of the data, the number of grip and release, the frequency response, and each finger’s correlation. Experiments with some grip-and-release patterns have resulted in different characteristics for each. It was suggested that this could be expressed in radar charts to intuitively know the state of grip and release. Contact-force data of each finger were found to be useful for understanding the characteristics of grip and release and improving the accuracy of calculating the number of times to grip and release. Frequency analysis suggests that knowing the periodicity of grip and release can detect unnatural grip and release and tremor states. The correlations between the fingers allow us to consider the finger’s grip-and-release characteristics, considering the hand’s anatomy. By taking these factors into account, it is thought that the 10-s grip-and-release test could give us a new value by objectively assessing the motor functions of the hands other than the number of times of grip and release.