Gait analysis on healthy subjects was performed based on surface electromyographic and acceleration sensor signal, implemented through machine learning approaches. The surface EMG and 3-axes acceleration signals have been acquired for 5 different terrains: level ground, ramp ascent, ramp descent, stair ascent, and stair descent. These signals were acquired from the tibialis anterior and gastrocnemius medial head muscles that correspond to dorsiflexion and plantar flexion, respectively. After feature extraction, these signals are fed to 5 conventional classifiers: linear discriminant analysis, k-nearest neighbors, decision tree, random forest, and support vector machine, that classify different terrains for human locomotion. We compared the classification results for the above classifiers with deep neural network classifier. The objective was to obtain the features and classifiers that are able to discriminate between 5 locomotion terrains with maximum classification accuracy in minimum time by acquiring the signal from the least number of leg muscles. The results indicated that the support vector machine gives the highest classification accuracy of 99.20 (± 0.80)% for the dataset acquired from 15 healthy subjects. In terms of both accuracy and computation time, the support vector machine outperforms other classifiers.
From the past few decades, the dietary modification in the treatment of a different diseases which indicates the physiological response to a food. However, the present study was reviewed systemically and it was found in the different studies that the daily consumption of different types of millet-based foods may reduce the risk of various disease such as Diabetes particularly TypeII, cardiovascular and so on. Even in few studies it was found that the Korean proso millet also elevates the HDL level along with effect on controlling glucose level. It can be concluded that millets play a vital role in management of type II diabetes, CVD and in various non-communicable diseases.
Analysis of EMG signal has been an interested topic in recent years for classifying surface myoelectric signal patterns. Myoelectric control is an unconventional method to control the upper limb prostheses, human-assisting robots and rehabilitation devices. The aim of present work is to assess the time-domain features of EMG signal for myoelectric control of upper extremity prostheses by utilizing scatter plot. Classification accuracy is calculated using linear discriminant classifier for different combination of feature vectors using principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques. Results show that willison amplitude and waveform length are the best features for separating the distinct upper-limb motions.
The wireless sensor network is one of the most significant technologies in the 21st century. So far wireless networking has been focused on high-speed and long range applications. However, there are many wireless monitoring and control applications for industrial and home environments which require longer battery life, lower data rates and less complexity than those existing standards. As an open and global standard for wireless sensor network, ZigBee shows advantages on low-cost, low power consumption and self forming. The ZigBee standard has been developed by the ZigBee Alliance based upon the IEEE 802.15.4 standard. The paper deals with the experimental results related to star topology implementation of wireless sensing network for process automation.
Purpose This paper aims to design and analyze a controlled magnetorheological damper-based ankle-foot prosthesis prototype. Design/methodology/approach The ankle-foot prostheses prototype is proposed using the lightweight three dimensional (3 D)-printed parts, MR damper and digital servomotor. Initially, the computer-aided design (CAD) model of the prosthetic foot, leaf spring, retention spring and the various connecting parts required to connect the pylon and damper actuator assemblies are designed using CAD software. Later, the fused deposition modeling 3 D printer-based technique prints a prosthetic foot and other connecting parts using Acrylonitrile Butadiene Styrene filament. The prototype consists of two control parts: the first part controls the MR actuator that absorbs the impacts during walking. The second part is the control of the electric actuator intended to generate the dorsiflexion and plantar flexion movements. Finally, the prototype is tested on a transtibial amputee under the supervision of a prosthetist. Findings The ANalysis SYStems software-based analysis has shown that the prosthetic foot has a factor of safety values between 4.7 and 8.7 for heel strike, mid-swing and toe-off; hence, it is safe from mechanical failure. The designed MR damper-based ankle-foot prosthesis prototype is tested on an amputee for a level-ground walk; he felt comfortable compared to his passive prosthesis. Originality/value The design of an MR damper-based prosthesis prototype offers a better dynamic range for locomotion than passive prostheses. It reduces the injuries and provides relief to the transtibial amputees.
Purpose The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent. Design/methodology/approach Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis. Findings The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains. Practical implications This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains. Originality/value The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.
This paper presents different machine learning techniques to classify the following foot movements: (i) dorsiflexion, (ii) plantarflexion, (iii) inversion, (iv) eversion, (v) medial rotation, and (vi) lateral rotation. The purpose is to design a real-time standalone computing system to predict the foot movements in the sagittal plane, useful for ankle-foot prosthesis control. Electromyography (EMG) and forcemyography (FMG) signals were acquired from the leg's tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and peroneus longus muscles. First, Raspberry Pi was used to acquire EMG/FMG signals and to classify foot movements in real-time using different machine learning techniques. Later, an Arduino Nano 33 BLE controller was employed to implement the TinyML algorithm to classify these foot movements in the Arduino environment. The results showed that Raspberry Pi-based classification provided more than 99.5% accuracy for the EMG signals using LDA, LR, KNN, and SVC classifiers for offline prediction. However, for the classification of real-time signals, the performance of LDA is exceptionally well in predicting all classes. For Arduino Nano 33 BLE controller, the TinyML algorithm performed the classification task in real-time (8.5msec) without any misclassification. Further, the classification accuracy using EMG signal is much better than FMG based classification. Finally, the TinyML algorithm is applied on a transtibial amputee, and it is found that all three classes were classified correctly. Our finding suggests that a TinyML based Arduino Nano 33 BLE microcontroller is comparatively faster to predict and control, and it is smaller in size, thus advantageous for real-time prosthetic leg control applications.
Agriculture is the global foundation of food security and economic stability, crucial for sustenance, employment, and environmental sustainability. Its significance transcends borders. Type of crop produced plays a key role in the agricultural yield. A key problem faced by the farmers is lack of knowledge of the type of crop to be produced as well as the amount of fertilizer required in their particular soil. Farmers think that the higher the fertilizer used, the greater the productivity. But it is not correct. The soil uses the exact amount it needs and leaves the rest. Over utilization leads to leaching and decrease in the natural soil fertility and many such problems. Also, always farming the same crop in the farmland makes the cropland barren; hence, the produce does not yield much profit for the farmers.
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