Human upper extremity has numerous functions in day-to-day life. Strokes lead to weakening in hand muscles because of which the patient is unable to hold an object properly. Stroke related problems are treated by physiotherapy and grip strength recovery is assessed by the devices such as Jamar dynamometer and pinch meter. In this paper, load cell and Force Sensing Resistor sensors are used which mimics the use of Jamar dynamometer and assesses the patient recovery quantitatively. The sensory unit consists of the load cell which is used to sense the data and give it to the Wheatstone bridge. Participant is asked to apply force on the developed hand dynamometer and the values of grip strength forces are recorded in the system. This device transmits the participant data wirelessly using the Bluetooth module. Load cell and Force Sensing Resistors (FSR) sensor data are stored in Rapid Miner and the graphs of grip strength are plotted. Thus, the developed prototype helps to determine the grip strength of a disabled person hand using low-cost devices and possible to compare the present and previous results of the assessment. The field of application of this tool is in physiotherapy and occupational therapy.
The activities of human can be classified into human actions, interactions, object–human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are surveillance environment systems, healthcare systems, military, patient monitoring system (PMS), etc., that involve interactions between electronic devices such as human–computer interfaces with persons. Initially, collecting the videos containing actions or interactions was performed by the humans. The given input videos were converted into number of frames, and then these frames were undergone preprocessing stage using by applying median filter. The noise of the given input frame is reduced by applying the median filter of the neighboring pixels. Through frames, desired features were extracted. The actions of the person which is recognised from the system is going to extract further. There are three spatial–temporal interest point (STIP) techniques such as Harris SPIT, Gabor SPIT and HOG SPIT used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the accuracy, sensitivity and specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represent how exactly the classifier categorizes its features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category.
Cognitive radio techniques are becoming very popular to overcome the spectrum scarcity. For dynamic channel access, spectrum latency is a major bottleneck to achieve good QoS. In this paper, we have proposed distributed proactive channel scanning for different channel assignment policies of primary users (PUs). From periodic channel scanning, secondary users (SUs) store recent history of channel occupancy, and based on that decide the order of channel selection when demand comes, and switch channels proactively, if any better channel appears. For various channel allocation strategies of primary users, different channel selection algorithms are presented for secondary users to improve the spectrum latency as well as the channel switching overhead per call. Through simulation, the performance of the proposed strategies is compared with that of conventional cognitive radio. Results show that by the proposed scheme, the call drop/block rate decreases by more than \(30\%\) with significant reduction in interference, and hence improving the QoS of the system.
The scheduling algorithms for IEEE 802.16 standard are designed with the predominant goals of throughput optimization, ensuring fairness and Quality of Service (QoS) provisioning. In this work, enhancements are proposed to the existing Weighted Fair Queuing (WFQ) and Deficit Weighted Round Robin (DWRR) scheduling algorithms to efficiently utilize the unused units. In WFQ, additional units may be assigned to a queue, thus reducing the service rate. Instead in Enhanced WFQ (EWFQ), multiple queues are served in a round by effectively utilizing the unexploited units. In DWRR, a queue is not serviced if the size of the packet at the front of the queue exceeds the available quantum. Enhanced DWRR (EDWRR) checks for packets with sizes less than the Deficit Counter (DC), sorts the queue and services a smaller packet in the current round. Further, if the queue that is currently served becomes empty, the DC is transferred to the ensuing active queue instead of making it zero. This helps in servicing more number of packets in a round. The proposed scheduling schemes are proficient in servicing specific traffic flows.
In this paper, an extractive text summarization approach using Assamese WordNet is proposed, and the difficulties faced while extracting summary in the Assamese document are discussed. The Assamese language is a low-level language. Synset is applied from Assamese WordNet. The various features used for identifying the most salient sentences to generate effective summary aspects such as TF-IDF, sentence length, sentence position and numerical identification are considered. Automatic Text Summarization in the Assamese language is still in an early stage and this language does not have its own approach. So, the text summarization approach is compared to the approaches applied in Bengali and Bangla language approaches as these languages share a script that is quite similar having slight variations in certain letters. The effectiveness of our proposed approach is demonstrated through a set of experiments carried out using ROUGE measure, and the evaluation is depicted in terms of Precision, Recall and F1-score.
