Achmad Arifin

Ankle and Knee Joint Angle Measurements during Gait with Wearable Sensor System for Rehabilitation

A method of joint angle measurement during gait using wearable sensors for rehabilitation was studied in this paper. The method corrected joint angles measured by gyroscopes using joint angles measured by accelerometers with Kalman filter. Although gyroscopes could measure joint angles, their offset drift caused error in measurement of joint angles. At first, we made up small sensor units consist of a gyroscope and an accelerometer. Then, the method was validated with the developed sensor units in measurement of ankle and knee joint angles of three healthy subjects under walking on short distance pathway and on treadmill for long time. The measured joint angles were compared with reference joint angles measured with optical motion measurement system simultaneously. The result for short distance walking showed low RMS errors and high correlation coefficients (3.19deg and 0.918 for the ankle joint, 2.98deg and 0.993 for the knee joint in average). The result for treadmill walking also showed low RMS errors and high correlation coefficients (3.04deg and 0.960 for the ankle, 4.19deg and 0.994 for the knee in average). Although the measurement accuracy decreased in some of trials of a specific subject because of sensor attachment position, the experimental results suggested that joint angle could be measured with good accuracy independent of measurement period of time, walking speeds and subjects.

Design of Fuzzy Controller of the Cycle-to-Cycle Control for Swing Phase of Hemiplegic Gait Induced by FES

The goal of this study was to design a practical fuzzy controller of the cycle-to-cycle control for multi-joint movements of swing phase of functional electrical stimulation (FES) induced gait. First, we designed three fuzzy controllers (a fixed fuzzy controller, a fuzzy controller with parameter adjustment based on the gradient descent method, and a fuzzy controller with parameter adjustment based on a fuzzy model) and two PID controllers (a fixed PID and an adaptive PID controllers) for controlling two-joint (knee and ankle) movements. Control capabilities of the designed controllers were tested in automatic generation of stimulation burst duration and in compensation of muscle fatigue through computer simulations using a musculo-skeletal model. The fuzzy controllers showed better responses than the PID controllers in the both control capabilities. The parameter adjustment based on the fuzzy model was shown to be effective when oscillating response was caused due to the inter-subject variability. Based on these results, we designed the fuzzy controller with the parameter adjustment realized using the fuzzy model for controlling three-joint (hip, knee, and ankle) movements. The controlled gait pattern obtained by computer simulation was not significantly different from the normal gait pattern and it could be qualitatively accepted in clinical FES gait control. The fuzzy controller designed for the cycle-to-cycle control for multi-joint movements during the swing phase of the FES gait was expected to be examined clinically.

Computer Simulation Test of Fuzzy Controller for the Cycle-to-Cycle Control of Knee Joint Movements of Swing Phase of FES Gait

We proposed a fuzzy control scheme to implement the cycle-to-cycle control for restoring swing phase of gait using functional electrical stimulation (FES). We designed two fuzzy controllers for the biceps femoris short head (BFS) and the vastus muscles to control flexion and extension of the knee joint during the swing phase. Control capabilities of the designed fuzzy controllers were tested and compared to proportional-integral-derivative (PID) and adaptive PID controllers in automatic generation of stimulation burst duration and compensation of muscle fatigue through computer simulations using a musculo-skeletal model. Parameter adaptations in the adaptive PID controllers did not significantly improve the control performance of the PID controllers. The fuzzy controllers were superior to the PID and adaptive PID controllers under several subject conditions and different fatigue levels. These results showed the fuzzy controller would be suitable to implement the cycle-to-cycle control of FES-induced gait.

A Feasibility Study of Fuzzy FES Controller Based on Cycle-to-Cycle Control: An Experimental Test of Knee Extension Control

Functional Electrical Stimulation (FES) can be effective in assisting or restoring paralyzed motor functions. The purpose of this study is to examine experimentally the fuzzy controller based on cycle-to-cycle control for FES-induced gait. A basic experimental test was performed on controlling maximum knee extension angle with normal subjects. In most of control trials, the joint angle was controlled well compensating changes in muscle responses to electrical stimulation. The results show that the fuzzy controller would be practical in clinical applications of gait control by FES. An automatic parameter tuning would be required practically for quick responses in reaching the target and in compensating the change in muscle responses without causing oscillating responses.

Fuzzy controller for cycle-to-cycle control of swing phase of FES-induced hemiplegic gait: a computer simulation in two-joints control

This paper described a test of fuzzy controller for controlling knee and ankle movements of swing phase of FES-induced hemiplegic gait. We developed five fuzzy controllers to control electrical stimulation for the hamstrings, the quadriceps, the gastrocnemius medialis, the tibialis anterior and the soleus muscles. The fuzzy controllers regulated burst durations of stimulation pulse trains to maintain certain maximum angles of knee flexion, knee extension, ankle plantar flexion, and ankle dorsiflexion and to prepare good initial contact. Capabilities of fuzzy controller in automatic generation of stimulation burst duration and compensating muscle fatigue were tested by computer simulation using musculo-skeletal model. The fuzzy controller generated standard burst duration in automatic generation of stimulation burst duration in a few cycles and compensated muscle fatigue fast.

Mixed vapour identification using partition column-QCMs and Artificial Neural Network

This Paper presents the identification of mixed vapour using electronic nose system composed of Quartz Crystal Microbalance (QCM) sensor array and a partition column of gas chromatography. The polymer coated QCMs produced a specific frequency shift. The data set was processed by an Artificial Neural Network using Backpropagation algorithm as a pattern recognition. The result showed that this equipment was able to identify five types of vapours namely benzene, acetone, isopropyl alcohol, non-polar and polar mixture (i.e. benzene and acetone), and also polar and polar mixture (i.e. isopropyl alcohol and acetone) with the identification rate of 96%.

Design of wearable system for closed-loop control of gait restoration system by Functional Electrical Stimulation

This paper describes design and test of a wearable FES system for the purpose of improving the performance of gait in patients with post-stroke. The prototype system that was developed includes electrical stimulator and sensor systems. Electrical stimulator was designed to generate pulse train that was realized using non-isolated boost converter. Sensor system was designed to measure gait phases that was realized using FSR sensors and to measure lower limb joint angles that was realized using a fusion of gyroscope and accelerometer-based tilt angle sensor. In order to remove measurement error due to bias error of the gyroscope and fluctuation of tilt sensor, Kalman filter was used to estimate true lower limb joint angles. Each system was tested separately. Testing was done by measuring the stimulators output on the tibialis anterior muscle stimulation in normal subjects. The characteristics of pulse train in accordance with the desired specifications and capable of producing contractions in the tibialis anterior muscle. Sensor system was tested to measure gait parameters in subjects who walk normally. Comparison of the measured data with existing research data, showed the same pattern of the signal, the magnitude value is still in the standard deviation value of comparative data.

A signal processing framework for multimodal cardiac analysis

The heart is a complex organ in the cardiovascular system which its measurement and analysis system in clinical level should be realized in an integrated system including all cardiac vital signs. A previous study combined ECG and PCG analysis could detect murmur symptom. However, the heart mechanical activity could not be described. This study developed a multimodal analysis of cardiac signals consisting of ECG signals, carotid pulse, and PCG. The purpose of this study was to develop and test an appropriate signal processing framework to facilitate parameter extraction and to enhance understanding of underlying mechanisms in the cardiac physiology. Frequency and time-frequency domain analysis of cardiac signals were performed to design sophisticated digital filters. Recursive digital filters were chosen in realizing segmentation methods and the advanced signal processing techniques were performed in parameter extraction. Results show the proposed method was able to detect QRS complex, P and T waves in ECG signal with 88% sensitivity and also percussion wave with 85.62% sensitivity. Sistolic (S1) and diastolic (S2) heart sound also could be separated. Classification of normal and the disease type of heart based on the cardiac parameters resulted by the presented signal processing framework would be next research topic.

Design of complex data acquisition system for kinetics, kinematics, and kinesiological gait analysis

Gait is one of biometrie information and defined as pattern movement of lower limb. Clinical gait analysis is important for treatment, diagnosis, and reference for therapy. This paper presents kinematics, kinetics analysis, and kinesiological of muscle signal using complex data acquisitions from normal subject. Gait data were taken using 4 instruments, Optotrak certus 3020, Vernier Force Plate, 8 channels self-design LE EMG, and Wearable sensor that consist of accelerometers, gyroscopes, and Force Sensitive Resistor (FSRs). The results of FSR (kinetics), wearable sensors (kinematics), LE-EMG (kinesiology), Optotrak Certus 3020 (kinematics) were integrated together so different results can be seen from the data. The data of LE hardware and LE software from muscle signal were compared. The analysis result of Force Plate was obtaining kinetics analysis, Fmax, Fmin, FTO, and FHO. This study was able to take gait data using 4 instruments simultaneously and kinetics, kinematics, and kinesiological gait analysis was succeed to made. This study presented gait data from 5 subjects. Further, data can be reproduced to get more normal and abnormal gait data and used for rehabilitation purpose.

The detection of organic solvent vapor by using polymer coated chemocapacitor sensor

A chemocapacitor consists of planar interdigital electrodes (IDE) made by two comb electrodes on a substrate. A dielectric film was applied on the electrodes in which the absorbed vapor will modify its permittivity. This study has fabricated chemocapacitor with the IDE distance of 0.5 mm, while the dielectric film was a sensitive layer consisting of a polymeric material. The deposition of the polymeric film was accomplished by drop casting. A sensor array consisting of four chemocapacitors coated with different polymers namely PEG-1540, PEG-20M, PEG-6000, and PVP was used to obtain the pattern of shift in the capacitance. The integrated circuit AD7746 was used as the capacitance to-digital converter (CDC). The organic solvents of ethanol, benzene, and aceton were used as the vapor samples in this experiment. The results showed that the change in the capacitance value increases proportionally to the concentration of vapour where sensors coated with PEG-1540 and PVP have higher sensitivity, i.e. 0.0028pF/part per thousand and 0.0027pF/part per thousand, respectively. Based on the capacitance to digital conversion capabilities, the system provides there solution of 0.4084ppm. The sensor array could produce a different pattern for each of the vapor sample. The Neural Network pattern recognition system could identify the type of vapor automatically with the root mean square error of 10-5