Control of triceps surae stimulation based on shank orientation using a uniaxial gyroscope during gait

This article presents a stimulation control method using a uniaxial gyroscope measuring angular velocity of the shank in the sagittal plane, to control functional electrical stimulation of the triceps surae to improve push-off of stroke subjects during gait. The algorithm is triggered during each swing phase of gait when the angular velocity of the shank is relatively high. Subsequently, the start of the stance phase is detected by a change of sign of the gyroscope signal at approximately the same time as heel strike. Stimulation is triggered when the shank angle reaches a preset value since the beginning of stance. The change of angle is determined by integrating angular velocity from the moment of change of sign. The results show that the real-time reliability of stimulation control was at least 95% for four of the five stroke subjects tested, two of which were 100% reliable. For the remaining subject, the reliability was increased from 50% found during the experiment, to 99% during offline processing. Our conclusion is that a uniaxial gyroscope on the shank is a simple, more reliable alternative to the heel switch for the purpose of restoring push-off of stroke subjects during gait

Validity and test-retest reliability of manual goniometers for measuring passive hip range of motion in femoroacetabular impingement patients.

<p>Abstract</p> <p>Background</p> <p>The aims of this study were to evaluate the construct validity (known group), concurrent validity (criterion based) and test-retest (intra-rater) reliability of manual goniometers to measure passive hip range of motion (ROM) in femoroacetabular impingement patients and healthy controls.</p> <p>Methods</p> <p>Passive hip flexion, abduction, adduction, internal and external rotation ROMs were simultaneously measured with a conventional goniometer and an electromagnetic tracking system (ETS) on two different testing sessions. A total of 15 patients and 15 sex- and age-matched healthy controls participated in the study.</p> <p>Results</p> <p>The goniometer provided greater hip ROM values compared to the ETS (range 2.0-18.9 degrees; <it>P </it>< 0.001); good concurrent validity was only achieved for hip abduction and internal rotation, with intraclass correlation coefficients (ICC) of 0.94 and 0.88, respectively. Both devices detected lower hip abduction ROM in patients compared to controls (<it>P </it>< 0.01). Test-retest reliability was good with ICCs higher 0.90, except for hip adduction (0.82-0.84). Reliability estimates did not differ between the goniometer and the ETS.</p> <p>Conclusions</p> <p>The present study suggests that goniometer-based assessments considerably overestimate hip joint ROM by measuring intersegmental angles (e.g., thigh flexion on trunk for hip flexion) rather than true hip ROM. It is likely that uncontrolled pelvic rotation and tilt due to difficulties in placing the goniometer properly and in performing the anatomically correct ROM contribute to the overrating of the arc of these motions. Nevertheless, conventional manual goniometers can be used with confidence for longitudinal assessments in the clinic.</p

eVM: An Event Virtual Machine Framework

International audienceInformation and communication technology (ICT) is impacting our daily lives more than ever before. Many existing applications guide users in their daily activities (e.g., navigation through traffic, health monitoring, managing home comfort, socializing with others). Although these applications are different in terms of purpose and application domain, they all detect events and propose actions and decision making aid to users. However, there is no usage of a common backbone for event detection that can be instantiated, re-used, and reconfigured in different use cases. In this paper, we propose eVM, a generic event Virtual Machine able to detect events in different contexts while allowing domain experts to model and define the targeted events prior to detection. eVM simultaneously considers the various features of the defined events (e.g., temporal, geographical), and uses the latter to detect different feature-centric events (e.g., time-centric, location-centric). eVM is based on different components (an event query language, a query compiler, an event detection core, etc.), but mainly the event detection modules are detailed here. We show that eVM is re-usable in different contexts and that the performance of our prototype is quasi-linear in most cases. Our experimental results showed that the detection accuracy is improved when, besides spatio-temporal information, other features are considered