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.

An instrumented object for hand exercise and assessment using a pneumatic pressure sensor.

Measurement of grip force is important for both exercise training and assessment of the hand during physical rehabilitation. The standard method uses a grip dynamometer which measures the force between the fingers and opposing thumb. The primary limitation of the grip dynamometer is the restriction of measurement to cylindrical grasps. Any deformation of the hand due to muscular or skeletal disease makes the grip dynamometer difficult or impossible to use. An alternative to the grip dynamometer is a sealed pneumatic object that can be gripped by the hand. Measurement of the internal pressure in the object can be related to the grip force. In this paper, we analyze such a pneumatic pressure sensing object for hand grip assessment and also describe an easy fabrication of the grip sensor. The instrumented object presented in this paper is designed to assess both the maximal voluntary grip forces and continuous grip force to monitor control of hand function during exercise under instruction from a therapist. Potential uses of such a pneumatic pressure sensing object for hand grip are in physical rehabilitation of patients following paralysing illnesses like stroke and spinal cord injury.

Wavelet decomposition of the blink reflex R2 component enables improved discrimination of multiple sclerosis.

OBJECTIVES: The blink reflex R2 component was subjected to wavelet decomposition for time feature extraction in order to classify the functional status of patients with multiple sclerosis. METHODS: The blink reflex was recorded bilaterally with unilateral stimulation of the supra-orbital nerve in 37 normal subjects and 9 patients with multiple sclerosis (MS). The late component, R2, was subjected to time-frequency decomposition using the Daubechies-4 wavelet. Using the time-frequency coefficients, the mean time of the R2 wave as well as the standard deviation of the R2 interval were calculated in each trial. The wavelet transform enables noise reduction by allowing selective use of frequency bands with high signal-to-noise ratio for time feature extraction; therefore automatic estimation of time parameters is robust. The distribution densities of the mean and the standard deviation of the R2 wave duration for the set of trials for each subject were computed. RESULTS: An appreciable difference in the densities of the two parameters extracted in the wavelet domain was seen between normals and patients. This is in contrast to the onset latency of R2 which poorly discriminates MS patients from normals. CONCLUSION: The results suggest that the mean and standard deviation of the R2-time robustly estimated using wavelet decomposition can be used to support clinical diagnosis in tracking the functional status of patients with diseases like multiple sclerosis.

Velocity of shortening of single motor units from rat soleus.

1. The force-velocity relationship of a motor unit can provide insight into the contractile proteins of its constituent fibers as well as fundamental information about the function and use of the motor unit. Although the force-velocity profiles of whole muscle and skinned mammalian fibers have been studied, technical difficulties have prevented similar studies on motor units. A technique is presented to directly measure the velocity of shortening of individual motor units from in vivo rat soleus muscle. 2. The soleus muscles of anesthetized rats were dissected free of surrounding tissue while their nerve and blood supplies were preserved. Both tendons were cut, and the distal tendon was attached to a servomechanism to control muscle length, whereas the proximal tendon was attached to a force transducer. Single motor units were stimulated via the ventral roots. 3. The major problem encountered in measuring the force-velocity profile of a motor unit was that the force from the large number of passive fibers and connective tissue in the soleus confounded the force produced by the small number of active fibers in the motor unit. This problem was minimized by measuring active motor unit tension during an isovelocity ramp. This allowed experimental measurement of the passive tension by shortening the muscle with an identical isovelocity ramp without, however, stimulating the motor unit. Active tension was estimated by subtracting the passive tension waveform from the waveform recorded when the motor unit was active. 4. The method substantially reduced the noise from the passive fibers; however, problems remained. The probable sources of error are discussed, with the most significant being the elasticity associated with the blood and nerve connections to surrounding tissue. The elasticity prevents uniform shortening velocities along the length of the active fibers, thereby introducing a systematic bias to measurements made at high velocities. These errors are most pronounced when the data are extrapolated to determine the maximum velocity of shortening (Vmax). Determination of velocity at peak power (Vpp) is a more robust measure; however, of the 34 motor units studied, only 19 exhibited a distinct peak in the power-force curve, indicating residual noise. 5. To assess the validity of using twitch contraction time as an index of the velocity of shortening, when possible, Vmax and Vpp of each motor unit were correlated with the inverse of its twitch contraction time. The correlation was poor (r less than 0.2), indicating that, although widely used, twitch contraction time is a poor index of contractile speed.