A low cost instrumented glove for extended monitoring and functional hand assessment

A Glove-Based Virtual Hand Rehabilitation System for Patients With Post-Traumatic Hand Injuries

Recent studies have shown that virtual gamified therapy can be a potential adjunct to conventional orthopedic rehabilitation. However, the off-the-shelf gaming consoles used for virtual rehabilitation pose several practical challenges in deploying them in clinical settings. In this article, we present the design of a portable glove-based virtual hand rehabilitation system (RehabRelive Glove) that can be used at both clinics and homes for physiotherapy. We also evaluate the system's efficacy on patients with post-traumatic hand injuries. Thirty patients were randomly categorized into groups A (virtual rehabilitation) and B (conventional physiotherapy). Both groups received fifteen 25-minute sessions of respective therapy over three weeks. The wrist and finger joints' range of motion (ROM) and grip strength were measured every seven sessions to compare the efficacy. Group A showed about 1.5 times greater improvement in flexion/extension ROM of the wrist compared to Group B. While both groups improved finger ROM and grip strength with time, no significant difference was observed between the groups. The results suggest that the proposed virtual rehabilitation system effectively enables patients with hand injuries to recover ROM faster.

Quantifying Hand Strength and Isometric Pinch Individuation Using a Flexible Pressure Sensor Grid

Modulating force between the thumb and another digit, or isometric pinch individuation, is critical for daily tasks and can be impaired due to central or peripheral nervous system injury. Because surgical and rehabilitative efforts often focus on regaining this dexterous ability, we need to be able to consistently quantify pinch individuation across time and facilities. Currently, a standardized metric for such an assessment does not exist. Therefore, we tested whether we could use a commercially available flexible pressure sensor grid (Tekscan F-Socket [Tekscan Inc., Norwood, MA, USA]) to repeatedly measure isometric pinch individuation and maximum voluntary contraction (MVC) in twenty right-handed healthy volunteers at two visits. We developed a novel equation informed by the prior literature to calculate isometric individuation scores that quantified percentage of force on the grid generated by the indicated digit. MVC intra-class correlation coefficients (ICCs) for the left and right hands were 0.86 (p < 0.0001) and 0.88 (p < 0.0001), respectively, suggesting MVC measurements were consistent over time. However, individuation score ICCs, were poorer (left index ICC 0.41, p = 0.28; right index ICC -0.02, p = 0.51), indicating that this protocol did not provide a sufficiently repeatable individuation assessment. These data support the need to develop novel platforms specifically for repeatable and objective isometric hand dexterity assessments.

Quantitative assessments of finger individuation with an instrumented glove

BACKGROUND

In clinical and research settings, hand dexterity is often assessed as finger individuation, or the ability to move one finger at a time. Despite its clinical importance, there is currently no standardized, sufficiently sensitive, or fully objective platform for these evaluations.

METHODS

Here we developed two novel individuation scores and tested them against a previously developed score using a commercially available instrumented glove and data collected from 20 healthy adults. Participants performed individuation for each finger of each hand as well as whole hand open-close at two study visits separated by several weeks. Using the three individuation scores, intra-class correlation coefficients (ICC) and minimal detectable changes (MDC) were calculated. Individuation scores were further correlated with subjective assessments to assess validity.

RESULTS

We found that each score emphasized different aspects of individuation performance while generating scores on the same scale (0 [poor] to 1 [ideal]). These scores were repeatable, but the quality of the metrics varied by both equation and finger of interest. For example, index finger intra-class correlation coefficients (ICC's) were 0.90 (< 0.0001), 0.77 (< 0.001), and 0.83 (p < 0.0001), while pinky finger ICC's were 0.96 (p < 0.0001), 0.88 (p < 0.0001), and 0.81 (p < 0.001) for each score. Similarly, MDCs also varied by both finger and equation. In particular, thumb MDCs were 0.068, 0.14, and 0.045, while index MDCs were 0.041, 0.066, and 0.078. Furthermore, objective measurements correlated with subjective assessments of finger individuation quality for all three equations (ρ = - 0.45, p < 0.0001; ρ = - 0.53, p < 0.0001; ρ = - 0.40, p < 0.0001).

CONCLUSIONS

Here we provide a set of normative values for three separate finger individuation scores in healthy adults with a commercially available instrumented glove. Each score emphasizes a different aspect of finger individuation performance and may be more uniquely applicable to certain clinical scenarios. We hope for this platform to be used within and across centers wishing to share objective data in the physiological study of hand dexterity. In sum, this work represents the first healthy participant data set for this platform and may inform future translational applications into motor physiology and rehabilitation labs, orthopedic hand and neurosurgery clinics, and even operating rooms.

A Curvature Sensor Utilizing the Matteucci Effect in Amorphous Wire

The study of wearable sensors for human disease monitoring has developed into an important research area due to its potential for personalized health care. Various sensor types have been proposed for assessing the range of joint movement in patients with progressive diseases or following post-surgical treatments. Many of these methods suffer from poor accuracy, sensitivity, and linearity or are very expensive and complex to implement. To overcome some of these limitations, this paper reports on the development of a novel flexible sensor for the measurement of bending by utilizing the Matteucci effect in the amorphous wire. This paper describes a bend sensor that utilizes positive magnetostrictive amorphous wire to achieve a measurement sensitivity equal to 5.68 ± 0.02 mV/cm with a resolution of ±0.2° over a measuring range of 64 to 143°.

Measurement of upper limb function in ALS: a structure review of current methods and future directions

Measurement of upper limb function is critical for tracking clinical severity in amyotrophic lateral sclerosis (ALS). The Amyotrophic Lateral Sclerosis Rating Scale-revised (ALSFRS-r) is the primary outcome measure utilised in clinical trials and research in ALS. This scale is limited by floor and ceiling effects within subscales, such that clinically meaningful changes for subjects are often missed, impacting upon the evaluation of new drugs and treatments. Technology has the potential to provide sensitive, objective outcome measurement. This paper is a structured review of current methods and future trends in the measurement of upper limb function with a particular focus on ALS. Technologies that have the potential to radically change the upper limb measurement field and explore the limitations of current technological sensors and solutions in terms of costs and user suitability are discussed. The field is expanding but there remains an unmet need for simple, sensitive and clinically meaningful tests of upper limb function in ALS along with identifying consensus on the direction technology must take to meet this need.

Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers' hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer's hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.

Design and Implementation of an Instrumented Data Glove that measures Kinematics and Dynamics of Human Hand

Human hands are versatile biomechanical architectures that can perform simple movements such as grasping to complicated movements such as playing a musical instrument. Such extremely dependable and useful parts of the human body can be debilitated due to movement disorders such as Parkinson's disease, stroke, spinal cord injury, multiple sclerosis and cerebral palsy. In such cases, precisely measuring the residual or abnormal hand function becomes a critical assessment to help clinicians and physical therapists in diagnosis, treatment and in prescribing appropriate prosthetics or rehabilitation therapies. The current methodologies used to measure abnormal or residual hand function are either paperbased scales that are prone to human error or expensive motion tracking systems. The cost and complexity restrict the usability of these methods in clinical environments. In this paper we present a low-cost instrumented glove that can measure kinematics and dynamics of human hand, by leveraging the recent advances in 3D printing technologies and flexible sensors.

High Compliance Pneumatic Actuators to Promote Finger Extension in Stroke Survivors

Compliant pneumatic systems are well suited for wearable robotic applications. The actuators are lightweight, conformable to irregular shapes, and tolerant of uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons given their space and mass constraints. Creating active digit extension with these exoskeletons is especially critical for clinical populations such as stroke survivors who often have great difficulty opening their paretic hand. To achieve active digit extension with a soft actuator, we have created pneumatic chambers that lie along the palmar surface of the digits. These chambers can directly extend the digits when pressurized. We present a characterization of the extension force and passive flexion resistance generated by these pneumatic chambers across a range of joint angles as a function of cross-sectional shape, dimension, and wall thickness. The chambers were fabricated out of DragonSkin 20 using custom molds and were tested on a custom jig. Extension forces created at the end of the chamber (where fingertip contact would occur) exceeded 3.00 N at relatively low pressure (48.3 kPa). A rectangular cross-section generated higher extension force than a semi-obround cross-sectional shape. Extension force was significantly higher (p < 0.05) for actuators with the highest wall thickness compared to those with the thinnest walls. In comparison to previously used polyurethane actuators, the DragonSkin actuators had a much higher extension force for a similar passive bending resistance. Passive bending resistance of the chamber (simulating finger flexion) did not vary significantly with actuator shape, wall thickness, width, or depth. The flexion resistance, however, could be significantly reduced by applying a vacuum. These results provide guidance in designing pneumatic actuators for assisting finger extension and resisting unwanted flexion in the fingers.

Wearables for Neurologic Conditions: Considerations for Our Patients and Research Limitations

Purpose of Review

In 2019, over 50 million Americans were expected to use wearables at least monthly. The technologies have varied capabilities, with many designed to monitor health conditions. We present a narrative review to raise awareness of wearable technologies that may be relevant to the field of neurology. We also discuss the implications of these wearables for our patients and briefly discuss issues related to researching new wearable technologies.

Recent Findings

There are a variety of wearables for neurologic conditions, e.g., stroke (for potential arrhythmia capture), epilepsy, Parkinson disease, and sleep. Research is being performed to capture the risk of neuropsychiatric relapse. However, data are limited and adherence to these wearables is often poorly studied.

Summary

The care of neurology patients may ultimately be improved with the use of wearable technologies. More research needs to examine efficacy and implementation strategies.

Heartrate variability biofeedback for migraine using a smartphone application and sensor: A randomized controlled trial

INTRODUCTION

Although hand temperature and electromyograph biofeedback have evidence for migraine prevention, to date, no study has evaluated heartrate variability (HRV) biofeedback for migraine.

METHODS

2-arm randomized trial comparing an 8-week app-based HRV biofeedback (HeartMath) to waitlist control. Feasibility/acceptability outcomes included number and duration of sessions, satisfaction, barriers and adverse events. Primary clinical outcome was Migraine-Specific Quality of Life Questionnaire (MSQv2).

RESULTS

There were 52 participants (26/arm). On average, participants randomized to the Hearthmath group completed 29 sessions (SD = 29, range: 2-86) with an average length of 6:43 min over 36 days (SD = 27, range: 0, 88) before discontinuing. 9/29 reported technology barriers. 43% said that they were likely to recommend Heartmath to others. Average MSQv2 decreases were not significant between the Heartmath and waitlist control (estimate = 0.3, 95% CI = -3.1 - 3.6). High users of Heartmath reported a reduction in MSQv2 at day 30 (-12.3 points, p = 0.010) while low users did not (p = 0.765).

DISCUSSION

App-based HRV biofeedback was feasible and acceptable on a time-limited basis for people with migraine. Changes in the primary clinical outcome did not differ between biofeedback and control; however, high users of the app reported more benefit than low users.

Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective

BACKGROUND

Parkinson's disease is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. There has been a rising interest in wearable devices for evaluation of movement disorder diseases such as Parkinson's disease due to the limitations in current clinic assessment methods such as Unified Parkinson's Disease Rating Scale (UPDRS) and the Hoehn and Yahr (HY) scale. However, there are only a few commercial wearable devices available, which, in addition, have had very limited adoption and implementation. This inconsistency may be due to a lack of users' perspectives in terms of device design and implementation. This study aims to identify the perspectives of healthcare professionals and patients linked to current assessment methods and to identify preferences, and requirements of wearable devices.

METHODS

This was a qualitative study using semi-structured interviews followed by focus groups. Transcripts from sessions were analysed using an inductive thematic approach.

RESULTS

It was noted that the well-known assessment process such as Unified Parkinson's Disease Rating Scale (UPDRS) was not used routinely in clinics since it is time consuming, subjective, inaccurate, infrequent and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices, particularly if they were involved in early design stages. Patients emphasized that the devices should be comfortable, but they did not have any concerns regarding device visibility or data privacy transmitted over the internet when it comes to their health. In terms of wearing a monitor, the preferable part of the body for all participants was the wrist. Healthcare professionals stated a need for an economical solution that is easy to interpret. Some design aspects identified by patients included clasps, material choice, and form factor.

CONCLUSION

The study concluded that current assessment methods are limited. Patients' and healthcare professionals' involvement in wearable devices design process has a pivotal role in terms of ultimate user acceptance. This includes the provision of additional functions to the wearable device, such as fall detection and medication reminders, which could be attractive features for patients.

Static accuracy of resistive bend sensors

Sensing of finger joint rotation can be difficult due to the hand's many degrees-of-freedom within a small space. Resistive bend sensors offer a potentially attractive option for making these measurements due to their low profile, small mass, and low cost. Further characterization of these bend sensors is needed, however, to ensure that readings are accurate. Specifically the static accuracy when bending and straightening the sensor has not been investigated. In this study, two-inch resistive sensors were bent from 0° to 90° and back to 0° while measuring the voltage output. Five calibration models were fitted to the measured data and used to determine the sensor's accuracy. When used with coated sensors, both the cubic and pchip models demonstrated non-monotonic behavior at low bend angles. Using uncoated sensors, the pchip calibration function and raw data resulted in a median error of 1.7° (SD 1.7°, range 12.1°).

A Platform for Rehabilitation of Finger Individuation in Children with Hemiplegic Cerebral Palsy

The brain injury that results in cerebral palsy CP may adversely affect fine motor control of the hand. The degradation of manual dexterity in the fingers profoundly impacts overall functionality of the upper limb, yet research efforts to facilitate rehabilitation of finger individuation in children with CP have been limited. This study describes the development of an integrated hardware and software platform for training and evaluating finger individuation. A pneumatically actuated glove provides extension assistance or flexion resistance independently to each digit in concert with playing a virtual reality keyboard. This setup enables intensive and efficient practice of fine motor control of either or both hands. Bimanual training options range from mirror movements to fully independent motions and rhythms in each hand, thereby enabling maintenance of the proper level of challenge. Additionally, an instrument was created to provide assessment of individuated fingertip force generation in order to evaluate effectiveness of the training. Preliminary data were obtained from children both with and without CP using this tool.

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.

An upper body garment with integrated sensors for people with neurological disorders - early development and evaluation

Background

In neurology and rehabilitation the primary interest for using wearables is to supplement traditional patient assessment and monitoring in hospital settings with continuous data collection at home and in community settings. The aim of this project was to develop a novel wearable garment with integrated sensors designed for continuous monitoring of physiological and movement related variables to evaluate progression, tailor treatments and improve diagnosis in epilepsy, Parkinson’s disease and stroke. In this paper the early development and evaluation of a prototype designed to monitor movements and heart rate is described. An iterative development process and evaluation of an upper body garment with integrated sensors included: identification of user needs, specification of technical and garment requirements, garment development and production as well as evaluation of garment design, functionality and usability. The project is a multidisciplinary collaboration with experts from medical, engineering, textile, and material science within the wearITmed consortium. The work was organized in regular meetings, task groups and hands-on workshops. User needs were identified using results from a mixed-methods systematic review, a focus group study and expert groups. Usability was evaluated in 19 individuals (13 controls, 6 patients with Parkinson’s disease) using semi-structured interviews and qualitative content analysis.

Results

The garment was well accepted by the users regarding design and comfort, although the users were cautious about the technology and suggested improvements. All electronic components passed a washability test. The most robust data was obtained from accelerometer and gyroscope sensors while the electrodes for heart rate registration were sensitive to motion artefacts. The algorithm development within the wearITmed consortium has shown promising results.

Conclusions

The prototype was accepted by the users. Technical improvements are needed, but preliminary data indicate that the garment has potential to be used as a tool for diagnosis and treatment selection and could provide added value for monitoring seizures in epilepsy, fluctuations in PD and activity levels in stroke. Future work aims to improve the prototype further, develop algorithms, and evaluate the functionality and usability in targeted patient groups. The potential of incorporating blood pressure and heart-rate variability monitoring will also be explored.

An instrumented glove for monitoring hand function

The measurement of hand kinematics is important for the assessment and rehabilitation of the paralysed hand. The traditional method of hand function assessment uses a mechanical or electronic goniometer placed across the joint of interest to measure the range of joint movement. Mechanical goniometers are imprecise and lack the ability to provide a dynamic measurement; electronic goniometers are expensive and cumbersome to use during therapy. An alternative to the goniometric based assessment is to use inertial motion sensors to monitor the hand movement-these can be incorporated in a glove. In this paper, we present the design of an instrumented glove equipped with Magnetic, Angular Rate and Gravity (MARG) sensors for the objective evaluation of hand function. The instrumented glove presented in this paper is designed to assess the range of movement of the hand and also monitor the hand function during the course of hand rehabilitation. Static and dynamic calibrations were performed for the Euler angles calculated from the MARG sensors. The results are also presented for physiological flexion/extension of the wrist (relative roll), flexion/extension of elbow (relative pitch), and internal rotation/external rotation (relative yaw). The static calibration results gave mean absolute errors of 4.1° for roll, 4.0° for pitch, and 4.6° for yaw. From the dynamic calibration, the speed of response to a step change gave a convergence time of 0.4 s; sinusoidally oscillating movement gave good tracking at 0.2 Hz but exhibits overshoot errors at higher frequencies which were tested to be 1 Hz. We present the results of the calibration of the instrumented glove (one sensor pair measuring one joint angle) measuring anatomical joint angles-mean absolute errors during static calibration: 6.3° for a relative roll (wrist flexion/extension), 5.0° for relative pitch (elbow flexion/extension), and 4.5° for relative yaw (shoulder internal rotation/external rotation). The experimental results from the instrumented glove are promising, and it can be used as an alternative to the traditional goniometer based hand function assessments.

A Wearable Detector for Simultaneous Finger Joint Motion Measurement

This paper presents a wearable and stretchable detector for simultaneous finger joint motion measurement, providing the feasible technical solutions to solve the problems existing in the current devices. The sensor array installed in the detector consists of 14 custom-made bending sensors and an inertial measurement unit with high accuracy. The glove support for a sensor array, which is made of three silicone materials with different elasticity, not only overcomes the drawbacks of cloth support, but also addresses the compatibility between the stretch substrate and the inelastic sensing components. Besides, two-section electrical connection avoids the problems caused by the external wires in traditional solutions to enhance the durability of glove. The experiment results indicated that the wearable detector offers a good user experience by exerting a small force on fingers. The device also shows a strong durability as it worked properly after tens of thousands of bending. The measurement tests on a single sensor demonstrated that two kinds of sensors are both with high accuracy. Besides, the hand goniometric system has an excellent performance in terms of hand motion detection. The measurement error of the device in subjects with different hand sizes is within a reasonable range. The good properties of the sensory detector allow it to be applied in clinical applications.

Data glove embedded with 9-axis IMU and force sensing sensors for evaluation of hand function

A hand injury can greatly affect a person's daily life. Physicians must evaluate the state of recovery of a patient's injured hand. However, current manual evaluations of hand functions are imprecise and inconvenient. In this paper, a data glove embedded with 9-axis inertial sensors and force sensitive resistors is proposed. The proposed data glove system enables hand movement to be tracked in real-time. In addition, the system can be used to obtain useful parameters for physicians, is an efficient tool for evaluating the hand function of patients, and can improve the quality of hand rehabilitation.

Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review

OBJECTIVES

Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke.

METHODS

A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables.

RESULTS

In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization.

CONCLUSIONS

Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.

New Device for Intrinsic Hand Muscle Strength Measurement: An Alternative to Strain Gauge Handheld Dynamometer

An accurate measurement of intrinsic hand muscle strength (IHMS) is required by clinicians for effective clinical decision-making, diagnosis of certain diseases, and evaluation of the outcome of treatment. In practice, the clinicians use Intrins-o-meter and Rotterdam Intrinsic Hand Myometer for IHMS measurement. These are quite bulky, expensive, and possess poor interobserver reliability (37–52%) and sensitivity. The purpose of this study was to develop an alternative lightweight, accurate, cost-effective force measurement device with a simple electronic circuit and test its suitability for IHMS measurement. The device was constructed with ketjenblack/deproteinized natural rubber sensor, 1-MΩ potential divider, and Arduino Uno through the custom-written software. Then, the device was calibrated and tested for accuracy and repeatability within the force range of finger muscles (100 N). The 95% limit of agreement in accuracy from −1.95 N to 2.06 N for 10 to 100 N applied load and repeatability coefficient of ±1.91 N or 6.2% was achieved. Furthermore, the expenditure for the device construction was around US$ 53. For a practical demonstration, the device was tested among 16 participants for isometric strength measurement of the ulnar abductor and dorsal interossei. The results revealed that the performance of the device was suitable for IHMS measurement.

A springs actuated finger exoskeleton: From mechanical design to spring variables evaluation

In the context of post-stroke patients, suffering of hemiparesis of the hand, robot-aided neuro-motor rehabilitation allows for intensive rehabilitation treatments and quantitative evaluation of patients' progresses. This work presents the design and evaluation of a spring actuated finger exoskeleton. In particular, the spring variables and the interaction forces between the assembly and the hand were investigated, in order to assess the effectiveness of the proposed exoskeleton.

A Two-Axis Goniometric Sensor for Tracking Finger Motion

The study of finger kinematics has developed into an important research area. Various hand tracking systems are currently available; however, they all have limited functionality. Generally, the most commonly adopted sensors are limited to measurements with one degree of freedom, i.e., flexion/extension of fingers. More advanced measurements including finger abduction, adduction, and circumduction are much more difficult to achieve. To overcome these limitations, we propose a two-axis 3D printed optical sensor with a compact configuration for tracking finger motion. Based on Malus’ law, this sensor detects the angular changes by analyzing the attenuation of light transmitted through polarizing film. The sensor consists of two orthogonal axes each containing two pathways. The two readings from each axis are fused using a weighted average approach, enabling a measurement range up to 180∘ and an improvement in sensitivity. The sensor demonstrates high accuracy (±0.3∘), high repeatability, and low hysteresis error. Attaching the sensor to the index finger’s metacarpophalangeal joint, real-time movements consisting of flexion/extension, abduction/adduction and circumduction have been successfully recorded. The proposed two-axis sensor has demonstrated its capability for measuring finger movements with two degrees of freedom and can be potentially used to monitor other types of body motion.

A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks

To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset. Considering the FMA scale is time-consuming and complicated, seven training exercises were designed to replace the upper limb related 33 items in FMA scale. 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital. Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data, the coefficient of determination can reach as high as 0.917. It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation.

Objective Surgical Skill Assessment: An Initial Experience by Means of a Sensory Glove Paving the Way to Open Surgery Simulation?

INTRODUCTION

Simulation and training in surgery are very promising tools for enhancing a surgeon's skill base. Accurate tracking of hand movements can be a strategy for objectively gauging a surgeon's dexterity, although "open" work is much more difficult to evaluate than are laparoscopic tasks. To the authors' knowledge, a system taking into account the movements of each finger joint has never been applied to open surgery simulation. This work intends to make up for this shortcoming and to perform a data analysis of the surgeon's entire gesture.

MATERIALS AND METHODS

The authors developed a sensory glove to measure flexion/extension of each finger joint and wrist movement. Totally 9 experts and 9 novices performed a basic suturing task and their manual performances were recorded within 2 days of measurements. Intraclass correlation coefficients were calculated to assess the ability of the executors to repeat and reproduce the proposed exercise. Wilcoxon signed-rank tests and Mann-Whitney U-tests were used to determine whether the 2 groups differ significantly in terms of execution time, repeatability, and reproducibility. Finally, a questionnaire was used to gather operators' subjective opinions.

RESULTS

The experts needed a similar reduced execution time comparing the 2 recording sessions (p = 0.09), whereas novices spent more time during the first day (p = 0.01). Repeatability did not differ between the 2 days, either for experts (p = 0.26) or for novices (p = 0.86). The 2 groups performed differently in terms of time (p < 0.001), repeatability (p = 0.01), and reproducibility (p < 0.001) of the same gesture. The system showed an overall moderate repeatability (intraclass correlation coefficient: experts = 0.64; novices = 0.53) and an overall high reproducibility. The questionnaire revealed performers' positive feedback with the glove.

CONCLUSIONS

This initial experience confirmed the validity and reliability of the proposed system in objectively assessing surgeons' technical skill, thus paving the way to a more complex project involving open surgery simulation.

Training finger individuation with a mechatronic-virtual reality system leads to improved fine motor control post-stroke

Background

Dexterous manipulation of the hand, one of the features of human motor control, is often compromised after stroke, to the detriment of basic functions. Despite the importance of independent movement of the digits to activities of daily living, relatively few studies have assessed the impact of specifically targeting individuated movements of the digits on hand rehabilitation. The purpose of this study was to investigate the impact of such finger individuation training, by means of a novel mechatronic-virtual reality system, on fine motor control after stroke.

Methods

An actuated virtual keypad (AVK) system was developed in which the impaired hand controls a virtual hand playing a set of keys. Creation of individuated digit movements is assisted by a pneumatically actuated glove, the PneuGlove. A study examining efficacy of the AVK system was subsequently performed. Participants had chronic, moderate hand impairment resulting from a single stroke incurred at least 6 months prior. Each subject underwent 18 hour-long sessions of extensive therapy (3x per week for 6 weeks) targeted at finger individuation. Subjects were randomly divided into two groups: the first group (Keypad: N = 7) utilized the AVK system while the other group (OT: N = 7) received a similarly intensive dose of occupational therapy; both groups worked directly with a licensed occupational therapist. Outcome measures such as the Jebsen-Taylor Hand Function Test (JTHFT), Action research Arm Test (ARAT), Fugl-Meyer Upper Extremity Motor Assessment/Hand subcomponent (FMUE/FMH), grip and pinch strengths were collected at baseline, post-treatment and one-month post-treatment.

Results

While both groups exhibited some signs of change after the training sessions, only the Keypad group displayed statistically significant improvement both for measures of impairment (FMH: p = 0.048) and measures of task performance (JTHFT: p = 0.021). Additionally, the finger individuation index – a measure of finger independence – improved only for the Keypad group after training (p = 0.05) in the subset (Keypad: N = 4; OT: N = 5) of these participants for which it was measured.

Conclusions

Actively assisted individuation therapy comprised of non task-specific modalities, such as can be achieved with virtual platforms like the AVK described here, may prove to be valuable clinical tools for increasing the effectiveness and efficiency of therapy following stroke.

Electronic supplementary material

The online version of this article (doi:10.1186/1743-0003-11-171) contains supplementary material, which is available to authorized users.

The Manumeter: a non-obtrusive wearable device for monitoring spontaneous use of the wrist and fingers

This paper describes the design and pilot testing of a novel device for unobtrusive monitoring of wrist and hand movement through a sensorized watch and a magnetic ring system called the manumeter. The device senses the magnetic field of the ring through two triaxial magnetometers and records the data to onboard memory which can be analyzed later by connecting the watch unit to a computer. Wrist and finger joint angles are estimated using a radial basis function network. We compared joint angle estimates collected using the manumeter to direct measurements taken using a passive exoskeleton and found that after a 60 minute trial, 95% of the radial/ulnar deviation, wrist flexion/extension and finger flexion/extension estimates were within 2.4, 5.8, and 4.7 degrees of their actual values respectively. The device measured angular distance traveled for these three joints within 10.4%, 4.5%, and 14.3 % of their actual values. The manumeter has potential to improve monitoring of real world use of the hand after stroke and in other applications.

Development and evaluation of a novel low-cost sensor-based knee flexion angle measurement system

BACKGROUND

Knee injuries form a large part of musculoskeletal trauma in sporting activities and the rehabilitation can require a long period, for both the patients and the specialists, to restore healthy condition. A reliable, portable, and low-cost system that could allow quick, simple, and effective measurement of knee flexion angles would greatly improve the evaluation of the rehabilitation process and the subsequent planning procedure, with meaningful reduction of recovery time and cost.

METHODS

A novel tool for nonstop measurements of knee flexion angles based on the adoption of an elastic sensor embedded in an easy-to-realize wearable kneepad has been proposed. We fully characterized this tool in terms of accuracy, repeatability, and reliability of measure, and validated it against the gold-standard Vicon.

RESULTS

Our tool demonstrated good reproducibility and repeatability among testers (mean range of measures=5.82° ± 1.93°) and high accuracy (root mean square error<1.28°), together with good reliability (intraclass correlation coefficient between 0.80 and 0.91).

CONCLUSIONS

The proposed tool demonstrates good performance, is portable, cheap, easy to use, and allows automatic measurements, so as to be a valuable system for accurate nonstop measurement of knee angles.

CLINICAL RELEVANCE

Our sensor-based measurement system is suitable for the evaluation of the rehabilitation course after knee traumas, because it furnishes a low-cost but accurate monitor of knee flexion movements, during an amount of time as long as desired.

Smart wearable body sensors for patient self-assessment and monitoring

Background

Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.

Methods

We reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians.

Results

Smart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine.

Conclusion

Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.

The manumeter: a wearable device for monitoring daily use of the wrist and fingers

Nonobtrusive options for monitoring the wrist and hand movement are needed for stroke rehabilitation and other applications. This paper describes the "manumeter," a device that logs total angular distance travelled by wrist and finger joints using a magnetic ring worn on the index finger and two triaxial magnetometers mounted in a watch-like unit. We describe an approach to estimate the wrist and finger joint angles using a radial basis function network that maps differential magnetometer readings to joint angles. We tested this approach by comparing manumeter estimates of total angular excursion with those from a passive goniometric exoskeleton worn simultaneously as seven participants completed a set of 12 manual tasks at low-, medium-, and high-intensity conditions on a first testing day, 1-2 days later, and 6-8 days later, using only the original calibration from the first testing day. Manumeter estimates scaled proportionally to the intensity of hand activity. Estimates of angular excursion made with the manumeter were 92.5% ± 28.4 (SD), 98.3% ± 23.3, and 94.7% ± 19.3 of the goniometric exoskeleton across the three testing days, respectively. Magnetic sensing of wrist and finger movement is nonobtrusive and can quantify the amount of use of the hand across days.

Assessment of hand kinematics using inertial and magnetic sensors

Background

Assessment of hand kinematics is important when evaluating hand functioning. Major drawbacks of current sensing glove systems are lack of rotational observability in particular directions, labour intensive calibration methods which are sensitive to wear and lack of an absolute hand orientation estimate.

Methods

We propose an ambulatory system using inertial sensors that can be placed on the hand, fingers and thumb. It allows a full 3D reconstruction of all finger and thumb joints as well as the absolute orientation of the hand. The system was experimentally evaluated for the static accuracy, dynamic range and repeatability.

Results

The RMS position norm difference of the fingertip compared to an optical system was 5±0.5 mm (mean ± standard deviation) for flexion-extension and 12.4±3.0 mm for combined flexion-extension abduction-adduction movements of the index finger. The difference between index and thumb tips during a pinching movement was 6.5±2.1 mm. The dynamic range of the sensing system and filter was adequate to reconstruct full 80 degrees movements of the index finger performed at 116 times per minute, which was limited by the range of the gyroscope. Finally, the reliability study showed a mean range difference over five subjects of 1.1±0.4 degrees for a flat hand test and 1.8±0.6 degrees for a plastic mold clenching test, which is smaller than other reported data gloves.

Conclusion

Compared to existing data gloves, this research showed that inertial and magnetic sensors are of interest for ambulatory analysis of the human hand and finger kinematics in terms of static accuracy, dynamic range and repeatability. It allows for estimation of multi-degree of freedom joint movements using low-cost sensors.

Synergy-based hand pose sensing: Optimal glove design

In this paper we study the problem of optimally designing glove-based sensing devices for hand pose reconstruction to maximize their potential for precision. In a companion paper we studied the problem of maximizing the reconstruction accuracy of the hand pose from partial and noisy data provided by any given pose sensing device (a sensorized “glove”) taking into account the knowledge of how humans most frequently use their hands in grasping tasks. In this paper we consider the dual problem of how to design pose sensing devices, i.e. how and where to place sensors on a glove, to get maximum information about the actual hand posture. We study the optimal design of gloves of different nature, according to a classification of current sensing technologies adopted in the domain. The objective is to provide, for given a priori information and fixed number of sensor inputs, the optimal design minimizing the reconstruction error statistics (assuming that optimal reconstruction algorithms are adopted). Finally, an experimental evaluation of the proposed method for optimal design is provided.

Synergy-based hand pose sensing: Reconstruction enhancement

Many applications in human–machine interfaces, information visualization, rehabilitation and entertainment require hand pose reconstruction systems that are both accurate and economic. Unfortunately, economically and ergonomically viable sensing gloves provide limited precision due to the imperfect and incomplete correspondence of sensing models with the anatomical degrees of freedom of the human hand, and because of measurement noise. This paper examines the problem of optimally estimating the posture of a human hand using non-ideal sensing gloves. The main idea is to maximize their performance by exploiting knowledge of how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware — hence basically at no extra cost — we propose the collection, organization, and exploitation of information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.

Smart wearable systems: current status and future challenges

OBJECTIVE

Extensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research.

METHODS

Herein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered.

RESULTS

We describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS.

CONCLUSION

We conclude by identifying the future challenges facing SWS for HM.

A new method of accurate hand- and arm-tracking for small primates

The investigation of grasping movements in cortical motor areas depends heavily on the measurement of hand kinematics. Currently used methods for small primates need either a large number of sensors or provide insufficient accuracy. Here, we present both a novel glove based on electromagnetic tracking sensors that can operate at a rate of 100 Hz and a new modeling method that allows to monitor 27 degrees of freedom (DOF) of the hand and arm using only seven sensors. A rhesus macaque was trained to wear the glove while performing precision and power grips during a delayed grasping task in the dark without noticeable hindrance. During five recording sessions all 27 joint angles and their positions could be tracked reliably. Furthermore, the field generator did not interfere with electrophysiological recordings below 1 kHz and did not affect single-cell separation. Measurements with the glove proved to be accurate during static and dynamic testing (mean absolute error below 2° and 3°, respectively). This makes the glove a suitable solution for characterizing electrophysiological signals with respect to hand grasping and in particular for brain-machine interface applications.

Design and evaluation of a low-cost instrumented glove for hand function assessment

Background

The evaluation of hand function impairment following a neurological disorder (stroke and cervical spinal cord injury) requires sensitive, reliable and clinically meaningful assessment tools. Clinical performance measures of hand function mainly focus on the accomplishment of activities of daily living (ADL), typically rather complex tasks assessed by a gross ordinal rating; while the motor performance (i.e. kinematics) is less detailed. The goal of this study was to develop a low-cost instrumented glove to capture details in grasping, feasible for the assessment of hand function in clinical practice and rehabilitation settings.

Methods

Different sensor types were tested for output signal stability over time by measuring the signal drift of their step responses. A system that converted sensor output voltages into angles based on pre-measured curves was implemented. Furthermore, the voltage supply of each sensor signal conditioning circuit was increased to enhance the sensor resolution. The repeatability of finger bending trajectories, recorded during the performance of three ADL-based tasks, was established using the intraclass correlation coefficient (ICC). Moreover, the accuracy of the glove was evaluated by determining the agreement between angles measured with the embedded sensors and angles measured by traditional goniometry. In addition, the feasibility of the glove was tested in four patients with a pathological hand function caused by a cervical spinal cord injury (cSCI).

Results

A sensor type that displayed a stable output signal over time was identified, and a high sensor resolution of 0.5° was obtained. The evaluation of the glove's reliability yielded high ICC values (0.84 to 0.92) with an accuracy error of about ± 5°. Feasibility testing revealed that the glove was sensitive to distinguish different levels of hand function impairment in cSCI patients.

Conclusions

The device satisfied the desired system requirements in terms of low cost, stable sensor signal over time, full finger-flexion range of motion tracking and capability to monitor all three joints of one finger. The developed rapid calibration system for easy use (high feasibility) and excellent psychometric properties (i.e. reliability and validity) qualify the device for the assessment of hand function in clinical practice and rehabilitation settings.

Enhancement of bend sensor properties as applied in a glove for use in neurorehabilitation settings

Following hand function impairment caused by a neurological disorder, the functional level of the upper extremities has to be assessed in the clinical and rehabilitation settings. Current hand function evaluation tests are somewhat imprecise. Instrumented gloves allow finger motion monitoring during the performance of skilled tasks, such as grasping objects. As a result, they provide an objective tool for evaluating slight changes in the fine motor skills of the hand. Numerous gloves are based on resistive bend sensors, given that this is an easy to handle, low-cost, and reliable sensing element. When bending is not applied homogeneously along such a sensor, as is the case with finger-joint bending, its output response varies with the sensor's longitudinal position. Our goal is to determine the optimal sensor position with respect to the finger-joint in order to enhance the resolution of the sensors embedded in a glove. The validity of the integrated sensors is evaluated and the accuracy values are given.

Use of a pneumatic glove for hand rehabilitation following stroke

Hand impairment is common following stroke and is often resistant to traditional therapy methods. Successful interventions have stressed the importance of repeated practice to facilitate rehabilitation. Thus, we have developed a servo-controlled glove to assist extension of individual digits to promote practice of grasp-and-release movements with the hand. This glove, the PneuGlove, permits free movement of the arm throughout its workspace. A novel immersive virtual reality environment was created for training movement in conjunction with the device. Seven stroke survivors with chronic hand impairment participated in 18 training sessions with the PneuGlove over 6 weeks. Overall, subjects displayed a significant 6-point improvement in the upper extremity score on the Fugl-Meyer assessment and this increase was maintained at the evaluation held one month after conclusion of all training (p < 0.01). The majority of this gain came from an increase in the hand/wrist score (3.8-point increase, p < 0.01). Thus, the system shows promise for rehabilitative training of hand movements after stroke.

An instrumented glove for small primates

The Cymanus is a novel flex sensor glove for measuring hand kinematics in primates. It was used to monitor 9 joints of a rhesus macaque performing a grasping task with 25 objects. Over 6 days, the monkey tolerated the glove and showed no significant impairment in performance. The sensors linearly tracked joint angles, with joint trajectories preserved over days. Angular positions discriminated objects as accurately as electromyograms recorded simultaneously from 24 arm and hand muscles, and were maximally informative of object identity at the end of reach-to-grasp. In a further validation of the glove, muscle activity controlling a joint was correlated with the joint's angular acceleration 70 ms later.

Measuring finger flexion and activity trends over a 25 hour period using a low cost wireless device

The goal of this research was to evaluate the usefulness of a wireless custom monitor in measuring real-time finger posture over an extended period of time as the wearer goes about daily life activities. One individual wore the device for 25 continuous hours in the research facility and at home. Initial data analysis methods have been presented to explore different aspects of hand activity over time. Evaluating mean flexion over five minute intervals reveals common hand postures assumed throughout the day, while total joint excursion over longer intervals highlights periods of inactivity associated with sleep or rest, as well as periods of higher intensity activities that can be used to evaluate compliance with physical therapy and home rehabilitation instructions.