|
|
Active prostetic hand
Brenner, Maximilian ; Sekora, Jiří (oponent) ; Harabiš, Vratislav (vedoucí práce)
BACKGROUND: Based on mainly vascular diseases and traumatic injuries, around 40,000 upper limb amputations are performed annually worldwide. The affected persons are strongly impaired in their physical abilities by such an intervention. Through myoelectric prostheses, affected persons are able to recover some of their abilities. METHODS: In order to control such prostheses, a system is to be developed by which electromyographic (EMG) measurements on the upper extremities can be carried out. The data obtained in this way should then be processed to recognize different gestures. These EMG measurements are to be performed by means of a suitable microcontroller and afterwards processed and classified by adequate software. Finally, a model or prototype of a hand is to be created, which is controlled by means of the acquired data. RESULTS: The signals from the upper extremities were picked up by four MyoWare sensors and transmitted to a computer via an Arduino Uno microcontroller. The Signals were processed in quantized time windows using Matlab. By means of a neural network, the gestures were recognized and displayed both graphically and by a prosthesis. The achieved recognition rate was up to 87% across all gestures. CONCLUSION: With an increasing number of gestures to be detected, the functionality of a neural network exceeds that of any fuzzy logic concerning classification accuracy. The recognition rates fluctuated between the individual gestures. This indicates that further fine tuning is needed to better train the classification software. However, it demonstrated that relatively cheap hardware can be used to create a control system for upper extremity prostheses.
|
|
|
Active prostetic hand
Brenner, Maximilian ; Sekora, Jiří (oponent) ; Harabiš, Vratislav (vedoucí práce)
BACKGROUND: Based on mainly vascular diseases and traumatic injuries, around 40,000 upper limb amputations are performed annually worldwide. The affected persons are strongly impaired in their physical abilities by such an intervention. Through myoelectric prostheses, affected persons are able to recover some of their abilities. METHODS: In order to control such prostheses, a system is to be developed by which electromyographic (EMG) measurements on the upper extremities can be carried out. The data obtained in this way should then be processed to recognize different gestures. These EMG measurements are to be performed by means of a suitable microcontroller and afterwards processed and classified by adequate software. Finally, a model or prototype of a hand is to be created, which is controlled by means of the acquired data. RESULTS: The signals from the upper extremities were picked up by four MyoWare sensors and transmitted to a computer via an Arduino Uno microcontroller. The Signals were processed in quantized time windows using Matlab. By means of a neural network, the gestures were recognized and displayed both graphically and by a prosthesis. The achieved recognition rate was up to 87% across all gestures. CONCLUSION: With an increasing number of gestures to be detected, the functionality of a neural network exceeds that of any fuzzy logic concerning classification accuracy. The recognition rates fluctuated between the individual gestures. This indicates that further fine tuning is needed to better train the classification software. However, it demonstrated that relatively cheap hardware can be used to create a control system for upper extremity prostheses.
|
host ::
RSS.