The human hand is one of the most complex structures of the human body, having the fingers that possess one of the highest numbers of nerve endings in the body. The hand, with its nerve endings has the capacity for the richest tactile feedback with excellent positioning capabilities.The existing hand control methods used in controlling smart prostheses have many drawbacks and need improvements. This paper proposes a new technique of controlling slippage, which is one of the major drawbacks for a prosthetic hand. A fuzzy logic control algorithm with multiple rules is designed along with a modified tactile sensory system for feedback. The slippage control acts as a complementary control system to the EMG or EEG based position control.A 5 Degrees of Freedom (DOF) hand was used which has one micro servo motor as actuator for each finger. A force sensing resistor is modified and used as a slippage sensor. First we use a reference EMG signal for getting the 5 DOF hand to grip an object, using position control. Then a slip is induced and we see the slippage control strategies work to hold the grasp. The results based on the plain sensory system and the modified system are discussed. Finally the advantages of the entire slippage control system are highlighted.
This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This MISO system model provides the estimated finger forces to be produced as input to the prosthetic hand. Optimal tracking method has been applied to track the estimated force profile of the Fusion based sEMG-force model. The simulation results show good agreement between reference force profile and the actual force.
Precise and effective prosthetic control is important for its applicability. Two desired objectives of the prosthetic control are finger position and force control. Variation in skeletal muscle force results in corresponding change of surface electromyographic (sEMG) signals. sEMG signals generated by skeletal muscles are temporal and spatially distributed that result in cross talk between adjacent sEMG signal sensors. To address this issue, an array of nine sEMG sensors is used with a force sensing resistor to capture muscle dynamics in terms of sEMG and skeletal muscle force. sEMG and skeletal muscle force are filtered with a nonlinear Teager-Kaiser Energy (TKE) operator based nonlinear spatial filter and Chebyshev type-II filter respectively. Multiple Takagi-Sugeno-Kang Adaptive Neuro Fuzzy Inference Systems (ANFIS) are obtained using sEMG as input and skeletal muscle force as output. Outputs of these ANFIS systems are fitted with smoothing spline curve fitting. To achieve better estimate of the skeletal muscle force, an adaptive probabilistic Kullback Information Criterion (KIC) for model selection based data fusion algorithm is applied to the smoothing spline curve fitting outputs. Final fusion based output of this approach results in improved skeletal muscle force estimates.
There is a need for more dynamic control over residential irrigation to conserve our precious water resource. This paper looks at developing a design for an Internet based "Smart Controller" for use in a residential sprinkler system to solve some problems that traditional controllers inherit. With the introduction of web based dynamic control brings new expense to the overall system however, this project yields a controller that would be very competitively priced among the current residential market for irrigation controllers while opening a new window of opportunity into water management and conservation.
Classical control methods for prosthetics have many drawbacks when it comes to controlling smart prosthetics. This paper uses electroencephalogram (EEG) signals, which have many advantages over electromyogram (EMG) signals, including the ability to integrate a robotic opposable thumb in a prosthetic hand. Servo motors are controlled by an embedded processor that responds to predetermined electro-potentials that are gathered using an EEG headset. The results are promising and the system proposed is a viable option for controlling a prosthetic hand's thumb.
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