Adaptive Restriction Rules Provide Functional and Safe Stimulation Pattern for Foot Drop Correction

Kostov, A.; Hansen, Morten Hjorslev; Haugland, Morten Kristian; Sinkjær, Thomas

doi:10.1046/j.1525-1594.1999.06375.x

Cited by 28 publications

(24 citation statements)

References 4 publications

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“…With the integration of a control algorithm, the timing of gait events can be identified. The detection mechanisms previously suggested varied from simple threshold detection to more advanced artificial intelligence, such as machine learning, fuzzy logic or neural networks [15][16][17][18][19][20][21]. Williamson et al used a single cluster of accelerometers attached to the shank for real-time gait event detection, and the results showed that the rule induction algorithms using rough sets and adaptive logic networks generated higher accuracy compared with rule-based algorithms [22].…”

Section: Introductionmentioning

confidence: 99%

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

2008

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Section: Introductionmentioning

confidence: 99%

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

2008

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“…4,5,13 Many strategies have been used to improve muscle recovery following these injuries, including distal motor neurotization, 14,24 electrical stimulation, 11,16 and pharmacological treatment 6,19 to increase the rate of reinnervation.…”

mentioning

confidence: 99%

Clinical application of sensory protection of denervated muscle

Bain

Hason

Veltri

et al. 2008

JNS

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Following proximal peripheral nerve injury, motor recovery is often poor due to prolonged muscle denervation and loss of regenerative potential. The transfer of a sensory nerve to denervated muscle results in improved functional recovery in experimental models. The authors here report the first clinical case of sensory protection. Following a total hip arthroplasty, this patient experienced a complete sciatic nerve palsy with no recovery at 3 months postsurgery and profound denervation confirmed electrodiagnostically. He underwent simultaneous neurolysis of the sciatic nerve and saphenous nerve transfers to the tibialis anterior branch of the peroneal nerve and gastrocnemius branch from the tibial nerve. He noted an early proprioceptive response. Electromyography demonstrated initially selective amelioration of denervation potentials followed by improved motor recovery in sensory protected muscles only. The patient reported clinically significant functional improvements in activities of daily living. The authors hypothesize that the presence of a sensory nerve during muscle denervation can improve functional motor recovery.Keywords muscle denervation; sciatic nerve palsy; sensory nerve Poor muscle and nerve functional recovery following proximal peripheral nerve injury can result in a serious clinical problem. Incomplete or inappropriate muscle reinnervation,

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“…To detect foot drop gait cycles more accurately, the rule induction algorithm was introduced to adapt the stimulation to foot drop gait. Kostov et al (1999) used Adaptive Logic Networks (ALN) to design a stance swing detector for FES control of hemiplegic gait. To detect the swing phase in foot drop gait, we recently developed a Neural Network and an acceleration sensor designed to be positioned on the thigh.…”

confidence: 99%

Clinical Application of Acceleration Sensor to Detect the Swing Phase of Stroke Gait in Functional Electrical Stimulation

Shimada

Ando

Matsunaga

et al. 2005

Tohoku J. Exp. Med.

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Functional electrical stimulation (FES) can improve the gait of stroke patients by stimulating the peroneal nerve in the swing phase of the affected leg, causing dorsiflexion of the foot that allows the toes to clear the ground. A sensor can trigger the electrical stimulation automatically during the stroke gait. We previously used a heel sensor system, which detects the contact pressure of the heel, in FES to correct foot drop gait. However, the heel sensor has disadvantages in cosmetics and durability. Therefore, we have replaced the heel sensor with an acceleration sensor that can detect the swing phase based on the acceleration speed of the affected leg, using a machine learning technique (Neural Network). We have used a signal for heel contact in a gait using the heel sensor before training with the Neural Network. The accuracy of the Neural Network detector was compared with a swing phase detector based on the heel sensor. The Neural Network detector was able to detect similarly the swing phase in the heel sensor. The largest difference in timing of the swing phase was less than 60 milliseconds in normal subjects and 80 milliseconds in stroke patients. We were able to correct foot drop gait using FES with an acceleration sensor and Neural Network detector. The present results indicate that an acceleration sensor positioned on the thigh, which is cosmetically preferable to systems in which the sensor is farther from the entry point of the electrodes, is useful for correction of stroke gait using FES.functional electrical stimulation; hemiplegia; foot drop; sensor; Neural Network

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Adaptive Restriction Rules Provide Functional and Safe Stimulation Pattern for Foot Drop Correction

Cited by 28 publications

References 4 publications

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot

Clinical application of sensory protection of denervated muscle

Clinical Application of Acceleration Sensor to Detect the Swing Phase of Stroke Gait in Functional Electrical Stimulation

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