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Improving independent mobility in people with various gait abnormalities is a major goal of rehabilitation therapy. While quantitative gait assessment is crucial to provide meaningful feedback on each treatment, many gait wearables have been validated with the standard method. However, none of the studies focused on validation of gait characterization in different classes of the pathological population compared with the control population. Hence, a novel wearable device called Gait Watch, worn on both the lower limbs, to estimate spatiotemporal and kinematic parameters of heterogeneous gait-impaired groups has been evaluated and its outcomes have been statistically analyzed. This article presents a statistical validation of gait dynamics of the control group and patients diagnosed with cerebral vascular accident, traumatic brain injury, peripheral nervous system, psychiatric issues, and seizures. Using the preliminary analysis results, a set of descriptive variables that allow for disease differentiation was selected to provide appropriate treatment to patients with various ailments and enhance their gait quality. The extraction of clinically significant gait parameters of interest using proposed Gait Watch would ascertain various neurological conditions as well as accurately quantify the extent of the difference in various gait parameters in subjects with different gait conditions when compared with the control group. [ABSTRACT FROM AUTHOR]

The development of the cloud plays a wide role in commencing secured communication among users. However, privacy and managing large databases is a major problem in a cloud model. This affects the data sharing due to a lack of trust amongst the stakeholders. Thus, we proposed a new blockchain-assisted authentication and access control technique to provide reliable data integrity verification between the cloud owners and the requester without relying on end-users. The proposed blockchain-based authentication and access control mechanism effectively increases the throughput and reduces the cost by addressing the issues of failure in the cloud model. This method integrates the Interplanetary File System (IPFS) and Ethereum blockchain for sharing the data effectively between the cloud owner and the requester. This framework distributes the secret key to the requester and the data is encrypted by adapting data policies to attain access controls over data. The keyword search function on ciphertext is executed on the basis of smart contract and Ethereum blockchain. The proposed Blockchain-based access control and authentication mechanism obtained better results with a genuine user detection rate of 95%, maximum search accuracy of 95%, and minimum responsiveness of 2.25 sec for 500 Blockchain size. [ABSTRACT FROM AUTHOR]

Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.

In today's globalized world, there is no transparency in exchanging data and information between producers and consumers. However, these tasks experience many challenges, such as administrative barriers, confidential data leakage, and extensive time delays. To overcome these challenges, we propose a decentralized, secured, and verified smart chain framework using Ethereum Smart Contract which employs Inter Planetary File Systems (IPFS) and MongoDB as storage systems to automate the process and exchange information into blocks using the Tendermint algorithm. The proposed work promotes complete traceability of the product, ensures data integrity and transparency in addition to providing security to their personal information using the Lelantos mode of shipping. The Tendermint algorithm helps to speed up the process of validating and authenticating the transaction quickly. More so in this time of pandemic, it is easier to meet the needs of customers through the Ethermint Smart Chain, which increases customer satisfaction, thus boosting their confidence. Moreover, Smart contracts help to exploit more international transaction services and provide an instant block time finality of around 5 sec using Ethermint. The paper concludes with a description of product storage and distribution adopting the Ethermint technique. The proposed system was executed based on the Ethereum-Tendermint Smart chain. Experiments were conducted on variable block sizes and the number of transactions. The experimental results indicate that the proposed system seems to perform better than existing blockchain-based systems. Two configuration files were used, the first one was to describe the storage part, including its topology. The second one was a modified file to include the test rounds that Caliper should execute, including the running time and the workload content. Our findings indicate this is a promising technology for food supply chain storage and distribution. [ABSTRACT FROM AUTHOR]

In the real world the QOS attribute delay is a research dispute that has raised much distress being pragmatic in the multi-hop wireless networks with huge amount of data to be transmitted. The objective is to enhance the throughput and accomplish lesser end-to-end delay at the same time. The prevailing work tries to condense the delay by regulating the packet flow that might control the throughput ratio, as well as congestion is also not considered in the case of multiple flows that share single route path for the transmission of data. In the proposed system, optimal data flow scheduling is conceded out with the function of reducing the end to end delay with maximized throughput ratio by modifiable packet flow ratio. This is performed by offering the novel algorithm stated to as dynamic optimized scheduling and congestion control in wireless network that can defer the optimal performance. This novel approach recovers the throughput and rapidity for wireless networks by altering scheduling scheme with virtual adaptation model. In this scheduling every slot is separated into mini slots to reduce the complexity. To control the congestion again the mini slots are separated as micro slots. Virtual rate adjustment is performed in a diverse manner for multiple flow links that exist within the network environment on the origin of their level of emergency, so as to present individual priority for the separate flow links. This technique succeeds in rendering optimal delay compared to the available research techniques in terms of enhanced performance ratio. [ABSTRACT FROM AUTHOR]