A Novel Setup and Protocol to Measure the Range of Motion of the Wrist and the Hand

Scaphocapitate arthrodesis with lunate preservation for Kienböck's disease: prospective outcomes study

PURPOSE

The study evaluated the efficacy of SC arthrodesis with lunate preservation for treating patients diagnosed with stage IIIB or IIIC Kienböck's disease, who also exhibit neutral ulnar variance. The study further aimed to explore potential variations in outcomes between patients diagnosed with stage IIIB and IIIC Kienböck's disease.

METHODS

Thirty-two patients diagnosed with stage IIIB (n = 19) and stage IIIC (n = 13) Kienböck's disease underwent SC arthrodesis with distal radius bone grafting stabilised by Herbert compression screws. All participants underwent pre- and post-operative assessments including VAS score for pain, ROM, grip strength, MMWS, and the Quick DASH score. Additionally, RS angle, LHI ratio, and CHI ratio were assessed.

RESULTS

For all patients, the mean operative time was 73 min, follow-up was 45.6 months, time to union was 14 weeks, and time to full return to work was 24 weeks. The rate of union at the arthrodesis site was 91% (29 out of 32 patients) whilst the incidence of postoperative degenerative arthritis was 36% (8 out of 32 patients). Regarding changes in the means of outcomes from pre- to post-operatively, the VAS score decreased from 8.2 to 1.3 and grip strength improved from 36 to 79%. The RS angle was corrected from 59° to 50°. Significant improvements were noted in the mean MMWS from 45 to 75 and QuickDASH score from 78 to 21. However, no significant changes were observed in ROM, LHI, and CHI. There were no significant differences between patients with stage IIIB and stage IIIC in terms of these parameters, except for differences observed in the RS angle, LHI, and CHI preoperatively and in LHI and CHI postoperatively.

CONCLUSION

Evidence level: II. Our research demonstrates that SC arthrodesis is a valuable approach for reducing pain, improving grip strength, and enhancing overall function in individuals with advanced Kienböck's disease. Importantly, our results indicate no notable differences in outcomes between patients diagnosed with stage IIIB or IIIC Kienböck's disease.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

The ageless approach: Nonoperative mastery competes head-on with surgery for elderly distal radius fractures

The aim of the current study is to compare the clinical outcomes of cast immobilization (CI) versus surgical treatment after 1 year for distal radius fractures (DRFs) in the elderly population. The cohort included patients aged 70-89 who suffered an acute, closed, and displaced DRF and who were treated conservatively or surgically at our clinic between August 2018 and January 2022. Those who had pathological fractures, open fractures, concomitant ulna fractures (except ulna styloid fractures), were not between the ages of 70 and 89, or refused to participate were excluded from the study. The study gathered data on patient demographics, initial radiological measurements, clinical measurements after 1 year, treatment models employed, and rates of complications. Of the total number of patients (276), CI was used on 77.2% (213), whereas the other 25 had volar-locked plates (VLP), 25 received external fixators with percutaneous pinning (EFPP), and 13 had isolated percutaneous pinning (IPP). 19 of 276 individuals had complications, with Complex Regional Pain Syndrome and Carpal Tunnel Syndrome being the most often documented. EFPP resulted in significantly higher Disability of the Arm, Shoulder, and Hand (DASH) score values than VLP and IPP at the 1st postoperative year (p < 0.05). No statistically significant difference was found between the DASH score and ROM values at the 1st postoperative year for patients who received CI versus those who underwent surgery (p > 0.05). In the first postoperative year, CI still retains its validity and performs similarly to surgery for DRFs in older individuals. VLPP and IPP methods outperformed EFPP surgeries.

Telerehabilitation Technology Used for Remote Wrist/Finger Range of Motion Evaluation: A Scoping Review

Background

Monitoring finger/wrist range of motion (ROM) is an important component of routine hand therapy after surgery. Telerehabilitation is a field that may potentially address various barriers of in-person hand therapy appointments. Therefore, the purpose of this scoping review is to identify telerehabilitation technologies that can be feasibly used in a patient's home to objectively measure finger/wrist ROM.

Methods

Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases using alternative word spellings for the following core concepts: "wrist/hand," "rehabilitation," and "telemedicine." Studies were imported into Covidence, and systematic two-level screening was done by two independent reviewers. Patient demographics and telerehabilitation information were extracted from the selected articles, and a narrative synthesis of the findings was done.

Results

There were 28 studies included in this review, of which the telerehabilitation strategies included smartphone angle measurement applications, smartphone photography, videoconference, and wearable or external sensors. Most studies measured wrist ROM with the most accurate technologies being wearable and external sensors. For finger ROM, the smartphone angle application and photography had higher accuracy than sensor systems. The telerehabilitation strategies that had the highest level of usability in a remote setting were smartphone photographs and estimation during virtual appointments.

Conclusions

Telerehabilitation can be used as a reliable substitute to in-person goniometer measurements, particularly the smartphone photography and motion sensor ROM measurement technologies. Future research should investigate how to improve the accuracy of motion sensor applications that are available on easy-to-access devices.

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.

Patient-Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System

Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient's progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand's mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient-therapist interaction and therapist-therapist data sharing.

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

BACKGROUND

Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images.

METHODS

Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots.

RESULTS

A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is -2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements.

CONCLUSIONS

The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.

Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation

Background

Radial, ulnar, or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to diagnose. However, without specialized knowledge, it is difficult for primary healthcare providers to recognize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment.

Methods

Based on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar, or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The receiver operating characteristic curve was employed for feature selection. We compared the performance of rule-based models with logistic regression, support vector machine and of random forest machine learning models by evaluating the accuracy, sensitivity, and specificity.

Results

The study included 1,344 images, twenty-two patients, and thirty-four volunteers. In rule-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2, 91.7, and 99.0% for radial nerve injury detection; (2) 97.3, 83.3, and 99.0% for ulnar nerve injury detection; and (3) 96.4, 87.5, and 97.1% for median nerve injury detection, respectively. All machine learning models had accuracy above 95% and sensitivity ranging from 37.5 to 100%.

Conclusion

Our study provides a helpful tool for detecting abnormal gestures in radial, ulnar, or median nerve injuries with satisfying accuracy, sensitivity, and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application.

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.

Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System

The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple joints. In terms of hardware, the system can adjust the light-irradiation angle and the light-irradiation intensity of the marker by actively adjusting the height of the light source to enhance the difference between the marker and the background and reduce the difficulty of segmenting the target marker and the background. In terms of algorithms, a combination of multiple-vision algorithms is used to compare the image-threshold segmentation and Hough outer- and inner linear detection as the knuckle-activity-range detection method of the system. To verify the accuracy of the visual-detection method, nine healthy volunteers were recruited for experimental validation, and the experimental results showed that the average angular deviation in the flexion/extension of the knuckle was 0.43° at the minimum and 0.59° at the maximum, and the average angular deviation in the adduction/abduction of the knuckle was 0.30° at the minimum and 0.81° at the maximum, which were all less than 1°. In the multi-angle velocimetry experiment, the time taken by the system was much less than that taken by the conventional method.

A Preliminary Study to Design and Evaluate Pneumatically Controlled Soft Robotic Actuators for a Repetitive Hand Rehabilitation Task

A stroke is an infarction in the cortical region of the brain that often leads to isolated hand paresis. This common side effect renders individuals compromised in their ability to actively flex or extend the fingers of the affected hand. While there are currently published soft robotic glove designs, this article proposed a unique design that allows users to self-actuate their therapy due to the ability to re-extend the hand using a layer of resistive flexible steel. The results showed a consistently achieved average peak of 75° or greater for each finger while the subjects’ hands were at rest during multiple trials of pneumatic assisted flexion. During passive assisted testing, human subject testing on 10 participants showed that these participants were able to accomplish 80.75% of their normal active finger flexion range with the steel-layer-lined pneumatic glove and 87.07% with the unlined pneumatic glove on average when neglecting outliers. An addition of the steel layer lowered the blocked tip force by an average of 18.13% for all five fingers. These data show strong evidence that this glove would be appropriate to advance to human subject testing on those who do have post stroke hand impairments.

Functional outcome measures for distal radius fractures: A systematic review

Objective: This systematic review aimed to identify and describe the utility of functional outcome measures reported in intervention trials between 2010 and 2020, and to map these measures to the International Classification of Functioning, Disability and Health (ICF) model.

Method: The search was carried out on MEDLINE, CINAHL and Cochrane Register of Clinical Trials. Peer-reviewed intervention studies detailing the functional outcome measures used for any treatment for distal radius fracture were selected. Participant characteristics, outcome measures reported and the trends in their use over time and geographical locations were extracted.

Results: This review analysed 119 studies. Thirty-one functional outcome measures were used across 36 countries. Ninety-two percent of studies measured both the Body Function/Structure and Activity/Participation domains of the ICF. The most frequently used measures were the Disabilities of the Arm, Shoulder and Hand Questionnaire, Range of Motion and Grip Strength. There is a lack of measures on successful return to meaningful occupation.

Conclusion: The outcome measures identified were equally spread across the ICF domains. There is a growing importance of Patient-Reported Outcome Measures to supplement performance-based measures, but a lack of measure on successful return to meaningful occupation.

Virtual reality as an intervention tool for upper limbs in Parkinson’s disease: a case series

ABSTRACT Parkinson’s disease (PD) is a neurodegenerative disorder in which dopaminergic loss occurs in the basal nuclei region. One major complaint associated with PD is upper extremity motor deficits (UE), frequently reported in difficulties to perform activities of daily living (ADL), which may negatively affect quality of life. In recent years new technologies have emerged to assist the UE rehabilitation process in PD, such as virtual reality. Therefore, this study sought to verify the effects of an intervention in the UE with semi-immersive virtual reality equipment on ADLs and quality of life of individuals with PD. Six individuals with PD were selected for intervention, and evaluated by the Mini Mental State Examination, the Hoehn & Yahr Scale, the Unified Parkinson’s Disease Rating Scale (UPDRS), the Parkinson’s Disease Questionnaire (PDQ-39) and the test d’évaluation des membres supérieurs de personnes âgées (TEMPA). The interventions lasted 27 minutes per session, twice per week, for 5 weeks, using the Leap Motion Controller. Individuals showed improvement in muscle strength, muscle endurance, ADLs, and quality of life, all statistically significant. In conclusion, the protocol based on virtual reality applied to the upper limbs effectively improved the activities of daily living and quality of life in individuals with PD.

Realidade virtual como ferramenta de intervenção para os membros superiores na doença de Parkinson: série de casos

RESUMO A doença de Parkinson (DP) é uma desordem neurodegenerativa na qual ocorre a perda dopaminérgica na região dos núcleos da base. Uma das principais queixas associadas à DP são os déficits motores dos membros superiores (MMSS) frequentemente relatados em dificuldades para realizar as atividades de vida diária (AVDs), podendo interferir negativamente na qualidade de vida. Nos últimos anos novas tecnologias surgiram para auxiliar no processo de reabilitação dos MMSS na DP, sendo a realidade virtual uma delas. Portanto, este estudo teve como objetivo verificar os efeitos de uma intervenção nos MMSS com equipamento de realidade virtual semi-imersiva nas AVDs e na qualidade de vida de indivíduos com DP. Foram selecionados seis indivíduos com DP para intervenção, avaliados por meio do miniexame do estado mental, da escala de Hoehn e Yahr, da escala unificada de avaliação para a DP (UPDRS), do questionário sobre a doença de Parkinson (PDQ-39) e do test d’évaluation des membres supérieurs de personnes âgées (Tempa). Seis sujeitos foram submetidos à intervenção com duração de 27 minutos por sessão, duas vezes na semana, por cinco semanas, utilizando o Leap Motion Controller. Obteve-se melhora na força muscular, na resistência muscular, nas AVDs e na qualidade de vida, todos com significância estatística. Dessa forma, verificou-se que o protocolo baseado em realidade virtual aplicada nos MMSS foi eficaz para melhorar as AVDs e a qualidade de vida dos indivíduos com DP deste estudo.

Analysis of the Leap Motion Controller's Performance in Measuring Wrist Rehabilitation Tasks Using an Industrial Robot Arm Reference

The Leap Motion Controller (LMC) is a low-cost markerless optical sensor that performs measurements of various parameters of the hands that has been investigated for a wide range of different applications. Research attention still needs to focus on the evaluation of its precision and accuracy to fully understand its limitations and widen its range of applications. This paper presents the experimental validation of the LMC device to verify the feasibility of its use in assessing and tailoring wrist rehabilitation therapy for the treatment of physical disabilities through continuous exercises and integration with serious gaming environments. An experimental set up and analysis is proposed using an industrial robot as motion reference. The high repeatability of the selected robot is used for comparisons with the measurements obtained via a leap motion controller while performing the basic movements needed for rehabilitation exercises of the human wrist. Experimental tests are analyzed and discussed to demonstrate the feasibility of using the leap motion controller for wrist rehabilitation.

The Black Box of Technological Outcome Measures: An Example in Duchenne Muscular Dystrophy

Background:

Outcome measures for non-ambulant Duchenne muscular dystrophy (DMD) patients are limited, with only the Performance of the Upper Limb (PUL) approved as endpoint for clinical trials.

Objective:

We assessed four outcome measures based on devices developed for the gaming industry, aiming to overcome disadvantages of observer-dependency and motivation.

Methods:

Twenty-two non-ambulant DMD patients (range 8.6–24.1 years) and 14 healthy controls (HC; range 9.5–25.4 years) were studied at baseline and 16 patients at 12 months using Leap Motion to quantify wrist/hand active range of motion (aROM) and a Kinect sensor for reached volume with Ability Captured Through Interactive Video Evaluation (ACTIVE), Functional Workspace (FWS) summed distance to seven upper extremity body points, and trunk compensation (KinectTC). PUL 2.0 was performed in patients only. A stepwise approach assessed quality control, construct validity, reliability, concurrent validity, longitudinal change and patient perception.

Results:

Leap Motion aROM distinguished patients and HCs for supination, radial deviation and wrist flexion (range p = 0.006 to <0.001). Reliability was low and the manufacturer’s hand model did not match the sensor’s depth images. ACTIVE differed between patients and HCs (p < 0.001), correlated with PUL (rho = 0.76), and decreased over time (p = 0.030) with a standardized response mean (SRM) of –0.61. It was appraised as fun on a 10-point numeric rating scale (median 9/10). PUL decreased over time (p < 0.001) with an SRM of –1.28, and was appraised as fun (median 7/10). FWS summed distance distinguished patients and HCs (p < 0.001), but reliability in patients was insufficient. KinectTC differed between patients and HCs (p < 0.01), but correlated insufficiently with PUL (rho = –0.69).

Conclusions:

Only ACTIVE qualified as potential outcome measure in non-ambulant DMD patients, although the SRM was below the commonly used threshold of 0.8. Lack of insight in technological constraints due to intellectual property and software updates made the technology behind these outcome measures a kind of black box that could jeopardize long-term use in clinical development.

Hand Measurement System Based on Haptic and Vision Devices towards Post-Stroke Patients

Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and computer presentation of essential parameters of a hand. Constructed software uses an integrated vision system, a haptic device for measurement, and has a web-based user interface. The system provides a simplified way to obtain hand parameters, such as hand size, wrist, and finger range of motions, using the homogeneous-matrix-based notation. The haptic device allows for active measurement of the wrist's range of motion and additional force measurement. A study was conducted to determine the accuracy and repeatability of measurements compared to the gold standard. The system functionality was confirmed on five healthy participants, with results showing comparable results to manual measurements regarding fingers' lengths. The study showed that the finger's basic kinematic structure could be measured by a vision system with a mean difference to caliper measurement of 4.5 mm and repeatability with the Standard Deviations up to 0.7 mm. Joint angle limits measurement achieved poorer results with a mean difference to goniometer of 23.6º. Force measurements taken by the haptic device showed the repeatability with a Standard Deviation of 0.7 N. The presented system allows for a unified measurement and a collection of important parameters of a human hand with therapist interface visualization and control with potential use for post-stroke patients' precise rehabilitation.

Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor

STUDY DESIGN

Cross-sectional study.

OBJECTIVE

To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device.

SUMMARY OF BACKGROUND DATA

Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time.

METHODS

In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score).

RESULTS

The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51.

CONCLUSION

Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine.Level of Evidence: 3.

Metacarpal Fractures, Management Techniques, and Outcomes in Our Center

Introduction/background

Metacarpal fractures comprise approximately 35.5% of cases in daily emergencies, mostly due to road traffic accidents (RTA), fall, and assault. The classification is based on the site and pattern of fracture. High-level evidence is lacking for the management of metacarpal fractures. The primary goals of treatment are to achieve acceptable alignment, stable reduction, strong bony union, and unrestricted motion. It can be managed by non-operative methods like close reduction and splintage. Operative management will be required if there is shortening, rotation, and angulation in different planes including close reduction and fixation with percutaneous intramedullary pining/k-wires and open reduction and fixation with screws, plates (compression/locking), and external fixators. This study was done to compare the efficacy of k-wire, screws, and plates in the management of metacarpal fractures and their outcomes based on their union, postoperative pain, range of movement, and grip strength in a tertiary care center, i.e., Liaquat National Hospital and Medical College.

Methods

It was a retrospective study conducted at the Department of Plastics and Reconstruction Surgery, of a tertiary care hospital. A total of 113 patients who were operated upon for metacarpal fracture were included in the study (open/close) without soft tissue loss or tendon injury, were divided into three groups according to the technique of fracture fixation, i.e., group 1 (k-wire), group 2 (screw), and group 3 (plates). The data like post-operative pain (visual analog scale, VAS) and radiological evidence of union were extracted from the registry. All the patients were called for follow-up in the outpatient department. Out of 113, 97 patients showed up for follow-up and were examined by a hand surgeon, and range of movement (goniometer) and grip strength (sphygmomanometer method) were assessed.

Results

A total of 97 patients were included in the study (male 66%, female 34%). Group 1 (K-wire) includes n = 61 (62.9%), group 2 (screw) n = 15 (15.5%), and group 3 (plate) n = 21 (21.6%). The mean follow-up time was 12 + 2 weeks after the surgery for post-operative pain and radiological evidence of union while 24 + 6 months for a range of movement and grip strength. Less post-operative pain was noted in group 1 patients while no significant difference was noted in the evidence of radiological union in all groups. Range of movement was better in group 1 patients (89.74 + 0.750) than in group 2 (80 + 0.37°) or group 3 (80.2 + 0.62°). The grip strength (compared to the normal contralateral hand) was normal in the majority of the patients in group 1, i.e., 94% while it was 80% in group 2 and 82% in group 3.

Conclusion

The significance of these reported findings suggests that open reduction and internal fixation with screw or plate might be a less preferable surgical technique in comparison to k-wire fixation in the treatment of a metacarpal fracture.

Evaluation of a multi-sensor Leap Motion setup for biomechanical motion capture of the hand

The Leap Motion controller (LMC) offers a low-cost means of markerless hand tracking, however, its utility is limited by a small field of view and reliance on appropriate sensor positioning. A recent update from Leap Motion has enabled the use of a multiple LMC device on a single computer, allowing the tracking of hands from multiple orientations, potentially overcoming the aforementioned limitations. This study describes a method of implementing a multi-LMC setup and evaluates its effect on the validity and reliability of the derived kinematics. This study implemented a Kabsch algorithm and Kalman filter to re-orientate and fuse the trajectories captured by three LMC at different orientations. Reliability was assessed by comparing between-day differences in maximum joint angles (ΔMJA) and a calculated coefficient of multiple correlations (CMC). Validity was assessed by comparing the LMC to the gold standard, a Vicon markered motion capture (MMC) system, and calculating the ΔMJA and applying the linear fit method. The proposed method was evaluated by comparing the reliability and validity of the single-LMC setups to the multi-LMC setup. A multi-LMC setup proved successful in improving the reliability and validity of kinematic data, most notably where reliability and validity were poor and variation was high between the single-LMC setups. Findings suggest that through implementing the proposed method, limitations associated with single-LMC setups, notably its reliance on optimal sensor positioning, can be overcome.

Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression

Continuous movements of the hand contain discrete expressions of meaning, forming a variety of semantic gestures. For example, it is generally considered that the bending of the finger includes three semantic states of bending, half bending, and straightening. However, there is still no research on the number of semantic states that can be conveyed by each movement primitive of the hand, especially the interval of each semantic state and the representative movement angle. To clarify these issues, we conducted experiments of perception and expression. Experiments 1 and 2 focused on perceivable semantic levels and boundaries of different motion primitive units from the perspective of visual semantic perception. Experiment 3 verified and optimized the segmentation results obtained above and further determined the typical motion values of each semantic state. Furthermore, in Experiment 4, the empirical application of the above semantic state segmentation was illustrated by using Leap Motion as an example. We ended up with the discrete gesture semantic expression space both in the real world and Leap Motion Digital World, containing the clearly defined number of semantic states of each hand motion primitive unit and boundaries and typical motion angle values of each state. Construction of this quantitative semantic expression will play a role in guiding and advancing research in the fields of gesture coding, gesture recognition, and gesture design.

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.

Estimation of spatial-temporal hand motion parameters in rehabilitation using a low-cost noncontact measurement system

Data collection and analysis are commonly used in a rehabilitation process to measure performances of the treatment. There is a lack of studies on the rehabilitation process monitored by a user-friendly interface. A low-cost system is developed in this research to assist users and therapists to measure hand motions and analyse important data of hand joints. The system consists of modules of data capturing, data analysis, and user interface. A Leap Motion sensor is used to capture joint positions of hand motions. Signal processing and wavelet de-noising methods are developed to improve accuracy of the data analysis. The user interface is designed using the Unity software to show graphical information of joint positions and motion parameters. The system has features of noncontact measurements, interactive environment, analysing and recording temporal data of motion parameters of hands. The system is validated by a gold standard motion capturing system. Case studies show effectiveness of the proposed system.

The validity of an objective measurement method using the Leap Motion Controller for fingers wrist, and forearm ranges of motion

The present study aimed to validate the Leap Motion Controller (LMC)-based Fizyosoft® HandROM System developed by our team to evaluate range of motion (ROM) for fingers, wrist, and forearm in a new clinical setting. Thirty-five healthy individuals participated in the study (all right-handed, 20-30 years old). The LMC-based Fizyosoft® HandROM System is a licensed software ROM-measurement developed by our team. Pronation/supination, wrist flexion/extension, ulnar/radial deviation and metacarpophalangeal (MCP) flexion/extension of all fingers were measured with both the Fizyosoft® HandROM System and a universal goniometer. No significant differences were found between the two measurement methods for almost all mean ROMs except for ulnar and radial deviation (p > 0.05). Highly significant correlations were found between all ROMs of the forearm, wrist, and thumb (p < 0.01). MCP flexion showed significant correlation only in the index finger (r = 0.516, p = 0.003) and little finger (r = 0.517, p = 0.004). Besides, for both measures, the intraclass correlations were good to excellent on all ROMs of the forearm, wrist, and fingers except for MCP of the middle and ring fingers (0.68-0.88). The present study results indicated that the LMC-based Fizyosoft® HandROM System could sensitively track changes in the active motion of the thumb, wrist, and forearm. It is a viable alternative for assessing ROMs of the forearm, wrist, and thumb in patient follow-up.

Leap Motion Controller Video Game-Based Therapy for Upper Extremity Motor Recovery in Patients with Central Nervous System Diseases. A Systematic Review with Meta-Analysis

Leap Motion Controller (LMC) is a virtual reality device that can be used in the rehabilitation of central nervous system disease (CNSD) motor impairments. This review aimed to evaluate the effect of video game-based therapy with LMC on the recovery of upper extremity (UE) motor function in patients with CNSD. A systematic review with meta-analysis was performed in PubMed Medline, Web of Science, Scopus, CINAHL, and PEDro. We included five randomized controlled trials (RCTs) of patients with CNSD in which LMC was used as experimental therapy compared to conventional therapy (CT) to restore UE motor function. Pooled effects were estimated with Cohen's standardized mean difference (SMD) and its 95% confidence interval (95% CI). At first, in patients with stroke, LMC showed low-quality evidence of a large effect on UE mobility (SMD = 0.96; 95% CI = 0.47, 1.45). In combination with CT, LMC showed very low-quality evidence of a large effect on UE mobility (SMD = 1.34; 95% CI = 0.49, 2.19) and the UE mobility-oriented task (SMD = 1.26; 95% CI = 0.42, 2.10). Second, in patients with non-acute CNSD (cerebral palsy, multiple sclerosis, and Parkinson's disease), LMC showed low-quality evidence of a medium effect on grip strength (GS) (SMD = 0.47; 95% CI = 0.03, 0.90) and on gross motor dexterity (GMD) (SMD = 0.73; 95% CI = 0.28, 1.17) in the most affected UE. In combination with CT, LMC showed very low-quality evidence of a high effect in the most affected UE on GMD (SMD = 0.80; 95% CI = 0.06, 1.15) and fine motor dexterity (FMD) (SMD = 0.82; 95% CI = 0.07, 1.57). In stroke, LMC improved UE mobility and UE mobility-oriented tasks, and in non-acute CNSD, LMC improved the GS and GMD of the most affected UE and FMD when it was used with CT.

Comparison of the Performance of the Leap Motion ControllerTM with a Standard Marker-Based Motion Capture System

Over the last few years, the Leap Motion Controller™ (LMC) has been increasingly used in clinical environments to track hand, wrist and forearm positions as an alternative to the gold-standard motion capture systems. Since the LMC is marker-less, portable, easy-to-use and low-cost, it is rapidly being adopted in healthcare services. This paper demonstrates the comparison of finger kinematic data between the LMC and a gold-standard marker-based motion capture system, Qualisys Track Manager (QTM). Both systems were time synchronised, and the participants performed abduction/adduction of the thumb and flexion/extension movements of all fingers. The LMC and QTM were compared in both static measuring finger segment lengths and dynamic flexion movements of all fingers. A Bland–Altman plot was used to demonstrate the performance of the LMC versus QTM with Pearson’s correlation (r) to demonstrate trends in the data. Only the proximal interphalangeal joint (PIP) joint of the middle and ring finger during flexion/extension demonstrated acceptable agreement (r = 0.9062; r = 0.8978), but with a high mean bias. In conclusion, the study shows that currently, the LMC is not suitable to replace gold-standard motion capture systems in clinical settings. Further studies should be conducted to validate the performance of the LMC as it is updated and upgraded.

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation

The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.

Camera-based Hand Tracking using a Mirror-based Multi-view Setup

Current clinical practice of measuring hand joint range of motion relies on a goniometer as it is inexpensive, portable, and easy to use, but it can only measure the static angle of a single joint at a time. To measure dynamic hand motion, a camera-based system that can perform markerless hand pose estimation is attractive, as the system is ubiquitous, low-cost, and non-contact. However, camera-based systems require line-of-sight, and tracking accuracy degrades when the joint is occluded from the camera view. Thus, we propose a multi-view setup using a readily available color camera from a single mobile phone, and plane mirrors to create multiple views of the hand. This setup eliminates the complexity of synchronizing multiple cameras and reduce the issue of occlusion. Experimental results show that the multi-view setup could help to reduce the error in measuring the flexion angle of finger joints. Dynamic hand pose estimation with object interaction is also demonstrated.

Application of a modified dorsoulnar artery pedicle flap in the repair of thumb tip defects: A case report

Between February 2012 and March 2014 10 patients were admitted to the Affiliated Hospital of Nantong University for surgery due to a thumb tip defect. Nine of these patients were male and one was female and there were 7 cases of left thumb defects and 3 cases of right thumb defects. The surgical procedures followed were first, modification of the flap tail to an equilateral triangle, which facilitated pedicle suturing of soft tissue defects, caused mild tension and effectively reduced venous disorders, and second complete opening of the flap pedicle to the soft tissue defects at the tunnel. All patients were followed up at 6 and 12 months after surgery. Grip and pinch strength were measured 6 and 12 months after surgery. Static two-point discrimination testing of the modified flap showed minor differences from the uninjured hand. Post-surgery grip and pinch strength were restored to approximately 85% of the level of that in the uninjured hand. The modified dorsoulnar artery pedicle flap provided excellent thumb tip defect coverage and is an effective and safe technique for the restoration of grip and pinch strength to the hand after the repair of a thumb tip defect.

Evaluation of the cognitive-motor performance of adults with Duchenne Muscular Dystrophy in a hand-related task

Duchenne muscular Dystrophy (DMD) is a progressive degenerative muscle disease, affecting, among others, the upper extremities. Effective hand rehabilitation can improve the hand function of people with DMD. To reach this goal, we first need to gain more insight into the hand cognitive-motor performance of people with DMD. This is the first study employing a systematic analysis on multi-finger, cognitive-motor performance of people with DMD. For this purpose, we propose an active dynamic visuo-motor task. The task employed six visual stimuli, a subset of which was activated at each trial. The stimuli were activated with a frequency of 1, 2, 3 and 4 Hz. Eight healthy participants and three participants with DMD performed the task. Additionally, the healthy participants performed seven sessions, and we assessed the training effects. Task-related cognitive-motor performance was evaluated using information transfer rate (ITR) and perceived workload. Regarding ITR, healthy participants performed significantly better than DMD participants; however, this was more evident for trials involving more than three fingers. Workload showed no difference between the healthy and the DMD groups. Healthy participants significantly improved their performance during training. Our results suggest that hand rehabilitation of people with DMD should consider multi-finger dynamic training. However, additional research with more people with DMD is needed for further generalization of our conclusions.

Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

Purpose

Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists.

Methods

A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements.

Results

High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006).

Conclusion

This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

Flex Sensor Compensator via Hammerstein-Wiener Modeling Approach for Improved Dynamic Goniometry and Constrained Control of a Bionic Hand

In this paper, a new control-centric approach is introduced to model the characteristics of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to wirelessly control a bionic hand. The main technique employs the inverse dynamic model strategy along with a black-box identification for the compensator design, which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. To smoothly recover the goniometry on the bionic hand's side during the wireless transmission, the compensator is restructured into a Hammerstein-Wiener model, which consists of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyze the performance of the proposed method. The associated temporal and spatial gesture data from both the glove and the bionic hand are recorded, and the performance is evaluated in terms of the integral of absolute error between the glove's and the bionic hand's dynamic goniometry. The proposed method is also compared with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Wiener model, which is based on the initial inverse dynamic design strategy. Experimental results with several trials for each gesture show that a great improvement is obtained via the Hammerstein-Wiener compensator approach where the resulting average errors are significantly smaller than the other two methods. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove, and thus provides a smooth human-robot collaboration with the bionic hand.