The Delsys Prize - Previous Winners
Facial EMG indices of empathy in children and adolescents
with disruptive behavior disorders
Dr. Anton van Boxtel, Associate Professor, Tilburg University, Department of Psychology
Innovation
Children and adolescents with disruptive behavior disorders are an important clinical group in child
psychiatry and psychology. A basic property of these disorders is a lack of emotional empathy. This deficit can be diagnosed by analysis of facial expressions in response to negative emotions displayed by others, using trained experts or automated systems. However, these methods have considerable weaknesses. Visual evaluation by experts is time consuming and rapid facial movements may be missed. A general problem of visual methods is that many facial emotional responses are relatively weak and remain under the visual detection threshold. Using facial EMG, visible and nonvisible responses can be accurately detected with a high time resolution. This application is novel in this field.
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Neural Oscillator Based Control For Pathological Tremor
Suppression Via Functional Electrical Stimulation
Dr. Dingguo Zhang, Associate Professor, Institute of Robotics, Shanghai Jiao Tong University
Innovation
This work is a novel application of the EMG signal at present, and it may also become a novel device or technique in near future. It is a promising technique to suppress pathological tremor with functional electrical stimulation (FES) compared with the traditional methods (medicine or surgery). We are the first group who adopts EMG signal as bio-feedback to design the biomimetic controller (neural oscillator) for tremor suppression via FES. EMG feedback can provide more instant information of tremor than other motion feedback information. In addition, we have proposed a technique named as two-step filter for EMG signal processing in this study.
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EMG-EMG Coherence During Muscle Fatigue
Dr. Madeleine Lowery, Senior Lecturer, School of Electrical, Electronic & Mechanical Engineering, University College Dublin
Innovation
Here we propose the analysis of coherence between simultaneously recorded EMG signals as a means of examining characteristics of common neural inputs to co-contracting muscles during fatiguing contractions. Coherence between surface EMG signals from the first dorsal interosseous and flexor digitoram superficialis was examined before and immediately following sustained fatiguing finger flexion. To enable changes during fatigue to be examined as a function of time, wavelet coherence was used to examine coherence between EMG signals recorded from the biceps brachii and brachioradialis during fatiguing elbow flexion. Application of coherence in this manner provides insight into the effects of fatigue on coherence and neuromuscular coupling across muscles, about which little is currently known.
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A Novel Trans-Urethral Electrode to Record EMG Activity of the Male Striated Urethral Sphincter
Mr. Ryan E. Stafford, PhD Student, Centre for Clinical Research Excellence in Spinal Pain, Injury and Health, University of Queensland, Australia
Innovation
The mechanism for continence in males is not completely understood. Until now, there has been no device for researchers and health professionals to investigate the specific function of the striated muscles within the male pelvic floor during functional tasks and upright body positions. Here we describe a new trans-urethral surface electromyography (EMG) electrode which has the potential to dramatically increase our understanding of the function of these muscles in both healthy males and also in men with altered pelvic floor musculature, such as those who undergo radical prostatectomy. This may, in turn, help to shape new treatment protocols for post-prostatectomy incontinence, which is a major side effect of the most common form of treatment for the most common form of malignant cancer in men.
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Toward neural control of artificial legs: a new strategy to identify locomotion modes using EMG
Dr. Helen (He) Huang of the University of Rhode Island, Kingston, RI, USA
Innovation
A phase-dependent EMG pattern classification strategy is proposed to promptly identify the user’s locomotion mode. This method was tested on both able-bodied subjects and subjects with long transfemoral amputations. The EMG signals from gluteal muscles and muscles in the thigh or residual limb contained sufficient neuromuscular control information for
reliable classification. This proposed approach has a great potential for design of neural-controlled, powered artificial
legs, which can further enhance the locomotion function in individuals with lower limb amputations.
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Decoding a new neural-machine interface for control of artificial limbs
Dr.Ping Zhou, Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, USA.
Summary
A new neural-machine interface, termed targeted muscle reinnervation (TMR), has been developed to improve the function of upper limb prostheses. The control information contained in TMR was assessed. Our results demonstrate that TMR can provide a rich source of motor control information and this information in turn promises to dramatically improve artificial arm function for people with proximal arm amputations.
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Probability-Based Prediction Of Electromyographic Activity In Multiple Muscles
Dr. Andrew Fuglivand, Departments of Physiology and Neurobiology, College of Medicine, University of Arizona, Tuscon, AZ, USA.
Summary
A probability-based method is proposed to predict the patterns of electromyographic activity across multiple muscles during a wide range of movements. A reasonable correspondence between predicted and actual EMG signals is demonstrated with this method. Such an approach ultimately might provide a flexible means to control functional electrical stimulation and thereby expand the repertoire of motor functions available to paralyzed individuals.
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Discriminating between normal and abnormal EMG profiles in walking
Dr. A.L. Hof, University of Groningen, Center for Human Movement Sciences, Groningen, The Netherlands
Summary:
A method is proposed to predict the normal EMG activity of human walking at any walking speed. The measured profile is compared to the predicted one and the deviation is calculated. In the deviation, temporal differences are taken heavily into account, while differences in amplitude, which are less relevant, are represented in a separate parameter. The method has already been proven useful in clinical research.
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Estimation of Proprioceptive Reflex Gains using Surface EMG
Professor Dr. F.C.T van der Helm, Delft University of Technology, Dept. of Mechanical Engineering, Delft, The Netherlands.
Summary:
Robot manipulators are being used to impose force perturbations to the hand. Hand position, hand force and EMG are being recorded. EMG signals are being pre-processed to estimate the dynamic relation to hand force. By optimizing the perturbation signal and using advanced closed loop system identification algorithms, the position, velocity and force feedback originating from the muscle spindles and Golgi tendon organs can be separated. Results were applied to patients with neurological disorders (CVA, Parkinson).
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Error Proofing Human Action in a Manufacturing Environment
Kim Sherman, Senior Project Engineer, Sandalwood.Summary
The current proposal is to use electromyography to monitor quality in automotive assembly plants. Providing hardware to monitor human movements throughout a work cycle can be used to ensure critical required work elements have been performed. The benefit of collecting data real-time while the movements are being performed will eliminate the need for non-value added quality checkpoints, additional equipment testing, and ensuring in station process control.
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