A Novel Multistandard Compliant Hand Function Assessment Method Using an Infrared Imaging Device

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects' upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score (r = 0.99, p < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.

Improved spatial-temporal graph convolutional networks for upper limb rehabilitation assessment based on precise posture measurement

After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.

Active Range of Motion Measurement System Using an Optical Sensor to Evaluate Hand Functions

Impairment of hand function greatly affects the independence of a human being. Proper assessment of hand function before and after any treatment for functional restoration is important to decide better treatment strategies. Despite traditional techniques of hand function evaluation, individual joint based assessment is vital to better track the details of the hand function. Current clinical assessments with goniometers are labour intensive, cumbersome and highly depend on the skill level of the practitioner. This study introduces an active range of motion (AROM) measurement system to measure individual range of motion of finger joints using an optical sensor. The proposed method is highly efficient, and the results demonstrated that the measurements are instant, repeatable and can successfully be employed in a clinical setup for patient evaluations.Clinical Relevance-Closely working with clinician to develop rehabilitation systems, we have identified that the assessment of patient hand functions is time consuming, and accuracy can be depended on the skill level of the practitioner in measuring joint range of motions (ROM). System introduced in this study can measure the joint AROMs instantly and independent of the practitioner's skill level and hence can provide a reliable, repeatable assessment of patient's hand function.

Precision and accuracy of measuring finger motion with a depth camera: a cross-sectional study of healthy participants

The purpose of this cross-sectional study was to determine the precision and accuracy of the measurement of finger motion with a depth camera. Fifty-five healthy adult hands were included. Measurements were done with a depth camera and compared with traditional manual goniometer measurements. Repeated measuring showed that the overall repeatability and reproducibility of extension measured with the depth camera were within 3° and 4° and that of flexion were within 13° and 14°. Compared with traditional manual goniometry, biases of extension of all finger joints and flexion of metacarpophalangeal joints were less than 5°, and the average bias of flexion of proximal and distal interphalangeal joints was 29°. We conclude that the measurement of finger extension and flexion of the metacarpophalangeal joints with a depth camera was reliable, but improvement is required in the precision and accuracy of interphalangeal joint flexion.

Validity and Reliability of a Depth Camera-Based Quantitative Measurement for Joint Motion of the Hand

Purpose

Quantitative measurement of hand motion is essential in evaluating hand function. This study aimed to investigate the validity and reliability of a novel depth camera-based contactless automatic measurement system to assess hand range of motion and its potential benefits in clinical applications.

Methods

Five hand gestures were designed to evaluate the hand range of motion using a depth camera-based measurement system. Seventy-one volunteers were enrolled in performing the designed hand gestures. Then, the hand range of motion was measured with the depth camera and manual procedures. System validity was evaluated based on 3 dimensions: repeatability, within-laboratory precision, and reproducibility. For system reliability, linear evaluation, the intraclass correlation coefficient, paired t -test and bias were employed to test the consistency and difference between the depth camera and manual procedures.

Results

When measuring phalangeal length, repeatability, within-laboratory precision, and reproducibility were 2.63%, 12.87%, and 27.15%, respectively. When measuring angles of hand motion, the mean repeatability and within-laboratory precision were 1.2° and 3.3° for extension of 5 digits, 2.7° and 10.2° for flexion of 4 fingers, and 3.1° and 5.3° for abduction of 4 metacarpophalangeal joints, respectively. For system reliability, the results showed excellent consistency (intraclass correlation coefficient = 0.823; P < .05) and good linearity with the manual procedures (r = 0.909-0.982, approximately; P < .001). Besides, 78.3% of the measurements were clinically acceptable.

Conclusions

Our depth camera-based evaluation system provides acceptable validity and reliability in measuring hand range of motion and offers potential benefits for clinical care and research in hand surgery. However, further studies are required before clinical application.

Clinical relevance

This study suggests a depth camera-based contactless automatic measurement system holds promise for assessing hand range of motion in hand function evaluation, diagnosis, and rehabilitation for medical staff. However, it is currently not adequate for all clinical applications.

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.

Sensorimotor Responses in Post-Stroke Hemiplegic Patients Modulated by Acupuncture at Yanglingquan (GB34): A fMRI Study Using Intersubject Functional Correlation (ISFC) Analysis

Motor dysfunction is common in patients with stroke. Acupuncture has become an acceptable alternative method for stroke rehabilitation. Previous studies have shown various functional connectivity changes activated by acupuncture. We introduced intersubject correlation (ISC) and intersubject functional correlation (ISFC) analyses into the functional magnetic resonance imaging (fMRI) for ischemic stroke to seek a common activation and suppression pattern triggered by acupuncture. In this study, 63 ischemic stroke patients with motor dysfunction and 42 normal controls were analyzed. Three functional scans were conducted during the resting state, motor task, and acupuncture at Yanglingquan (GB34) task. Twenty-two sensory, motor, and movement-imagination cortices in the bilateral hemispheres were selected as the region of interest (ROI). We performed ISC and ISFC analyses among these ROIs in three fMRI runs on patients and controls. Subgroup analyses by course or severity were also conducted. The results showed that acupuncture at GB34 triggered ISFC among upper limb motor, upper limb/hand/face, lower limb, tongue/larynx sensory, and movement imagination regions in the patient group. Subgroup ISC and ISFC analyses showed that patients tended to have increasing responses in the early stage of stroke (within 1 month) and decreasing responses afterward (1–3 months). Patients with mild clinical functional damage (NIHSS 2–4) tended to generate more responses via acupuncture than those with moderate damage (NIHSS 5–15). Our findings may help understand the clinical effects and modulatory features of acupuncture based on the group-level post-stroke neuroplasticity.

[Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand]

OBJECTIVE

To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand.

METHODS

Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy.

RESULTS

The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05).

CONCLUSION

The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.

Occupational Therapy Assessment for Upper Limb Rehabilitation: A Multisensor-Based Approach

Most post-stroke patients experience varying degrees of impairment in upper limb function and fine motor skills. Occupational therapy (OT) with other rehabilitation trainings is beneficial in improving the strength and dexterity of the impaired upper limb. An accurate upper limb assessment should be conducted before prescribing upper limb OT programs. In this paper, we present a novel multisensor method for the assessment of upper limb movements that uses kinematics and physiological sensors to capture the movement of the limbs and the surface electromyogram (sEMG). These sensors are Kinect, inertial measurement unit (IMU), Xsens, and sEMG. The key assessment features of the proposed model are as follows: (1) classification of OT exercises into four classes, (2) evaluation of the quality and completion of the OT exercises, and (3) evaluation of the relationship between upper limb mobility and muscle strength in patients. According to experimental results, the overall accuracy for OT-based motion classification is 82.2%. In addition, the fusing of Kinect and Xsens data reveals that muscle strength is highly correlated with the data with a correlation coefficient (CC) of 0.88. As a result of this research, occupational therapy specialists will be able to provide early support discharge, which could alleviate the problem of the great stress that the healthcare system is experiencing today.