EXPERIMENTAL INVESTIGATION ON DRYING OF BLANCHED BITTER GOURD SLICES IN A TRIPLE PASS SOLAR DRYER

This paper involves with the experimental performance study of a triple pass solar flat plate collector integrated with  thermal energy storage medium for drying of blanched bitter gourd slices. The experimental setup consists of a blower, triple pass flat plate collector and a drying chamber. The experimets were carried out in the meterological conditions of Coimbatore, Tamil Nadu, India. The mass flow rate of air through the system was set as 0.06 kgs-1. From the results, it could be comprehended that, (i) triple pass arrangement of air improves the performance of flat plate collector (ii) thermal energy storage medium played an important role in consistent outlet air temperature and the thermal efficiency (iii) triple pass solar dryer took 5 hours to reach the final moisture content of bitter gourd (9% on wet basis) which is 29 % and 37.5% faster than without thermal storage and open sun drying system. It could be concluded that triple pass solar air collector performs better than without thermal storage and open sun drying

Computation and evaluation of features of surface electromyogram to identify the force of muscle contraction and muscle fatigue

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P<0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P<0.01). Both of these features were not affected by the intersubject variations (P<0.05)

Improvement of isometric dorsiflexion protocol for assessment of tibialis anterior muscle

It is important to accurately estimate the electromyogram (EMG)/force relationship of triceps surae (TS) muscle for detecting strength deficit of tibalis anterior (TA) muscle. In literature, the protocol for recording EMG and force of dorsiflexion have been described, and the necessity for immobilizing the ankle has been explained. However, there is a significant variability of the results among researchers even though they report the fixation of the ankle. We have determined that toe extension can cause significant variation in the dorsiflexion force and EMG of TS and this can occur despite following the current guidelines which require immobilizing the ankle. The results also show that there was a large increase in the variability of the force and the RMS of EMG of TS when the toes were not strapped compared with when they were strapped. Thus, with the current guidelines, where there are no instructions regarding the necessity of strapping the toes, the EMG/force relationship of TS could be incorrect and give an inaccurate assessment of the dorsiflexor TA strength. In summary, - Current methodology to estimate the dorsiflexor TA strength with respect to the TS activity, emphasizing on ankle immobilization is insufficient to prevent large variability in the measurements. - Toe extension during dorsiflexion was found to be one source of variability in estimating the TA strength. - It is recommended that guidelines for recording force and EMG from TA and TS muscles should require the strapping of the toes along with the need for immobilizing the ankle

Towards identification of finger flexions using single channel surface electromyography - able bodied and amputee subjects

This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity. Methods: SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location. Results: Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation. Conclusions: Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification

Selection of suitable hand gestures for reliable myoelectric human computer interface

Background: Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. Methods: Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. Results: When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion. Conclusion: This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor

Pattern classification of Myo-Electrical signal during different maximum voluntary contractions: A study using BSS techniques

presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable

Fractal features of surface electromyogram: a new measure for low level muscle activation

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during maintained wrist and finger flexion. It has been established in literature that surface electromyogram (sEMG) and other such biosignals are fractal signals. Some researchers have determined that fractal dimension (FD) is related to strength of muscle contraction. On careful analysis of fractal properties of sEMG, this research work has established that FD is related to the muscle size and complexity and not to the strength of muscle contraction. The work has also identified a novel feature, maximum fractal length (MFL) of the signal, as a good measure of strength of contraction of the muscle. From the analysis, it is observed that while at high level of contraction, root mean square (RMS) is an indicator of strength of contraction of the muscle, this relationship is not very strong when the muscle contraction is less than 50% maximum voluntary contraction. This work has established that MFL is a more reliable measure of strength of contraction compared to RMS, especially at low levels of contraction. This research work reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that fractal dimension (FD) of the signal is related with the properties of the muscle while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that classifying MFL and FD of a single channel sEMG from the forearm it is possible to accurately identify a set of finger and wrist flexion based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for human computer interface