Background: Identifying user suitability plays a vital role in various modalities like
neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper
analysis the user suitability based on neural networks (NN), subjects, age groups and gender
for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand.
Six parametric feature extraction algorithms are used to extract the features from sEMG signals
such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance,
Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using
Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural
Methods: sEMG signals generated from forearm muscles of the participants are collected through
an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the
user is identified in the sEMG recognition module using signal processing, feature extraction and
machine learning techniques. The information about the identified movement is passed to microcontroller
wherein a control is developed to command the prosthetic hand to emulate the identified
Results: From the six feature extraction algorithms and two neural network models used in the
study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern
Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best
suited for this study as the neural network model is specially designed for pattern matching problem.
Moreover, it has simple architecture and low computational complexity. AR Burg is found to
be the best feature extraction technique in this study due to its high resolution for short data records
and its ability to always produce a stable model. In all the neural network models, the
maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness
and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are
best suited for the study due to their better muscle contractions. Better muscle fatigue resistance
has contributed for better performance of female subjects as compared to male subjects. From the
single trial analysis, it can be observed that the hand close movement has achieved best recognition
rate for all neural network models.
Conclusion: In this paper a study was conducted to identify user suitability for designing hand
prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements.
The suitability of the user was identified using two neural networks with six parametric features.
From the result, it was concluded thatfit women doing regular physical exercises aged between
26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition
Neural Network with AR Burg extraction features using extension movements will be a better
way to design the HMI. However, Signal acquisition based on wireless method is worth considering
for the future.