Published version made available following 12 month embargo from the date of publication [12 Feb 2015] according to publisher policy. Accessed 10/03/2015. Published version available from 13 February 2016.

/pdf/virtual-reality-for-stroke-rehabilitation-4evwp7ler3.pdf

Virtual reality for stroke rehabilitation

The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.

https://www.mdpi.com/1424-8220/12/8/11113/pdf

A Survey on Clustering Routing Protocols in Wireless Sensor Networks

Enabled by mobile and wearable technology, personal health data delivers immense and increasing value for healthcare, benefiting both care providers and medical research The secure and convenient sharing of personal health data is crucial to the improvement of the interaction and collaboration of the healthcare industry Faced with the potential privacy issues and vulnerabilities existing in current personal health data storage and sharing systems, as well as the concept of self-sovereign data ownership, we propose an innovative user-centric health data sharing solution by utilizing a decentralized and permissioned blockchain to protect privacy using channel formation scheme and enhance the identity management using the membership service supported by the blockchain A mobile application is deployed to collect health data from personal wearable devices, manual input, and medical devices, and synchronize data to the cloud for data sharing with healthcare providers and health insurance companies To preserve the integrity of health data, within each record, a proof of integrity and validation is permanently retrievable from cloud database and is anchored to the blockchain network Moreover, for scalable and performance considerations, we adopt a tree-based data processing and batching method to handle large data sets of personal health data collected and uploaded by the mobile platform

Integrating blockchain for data sharing and collaboration in mobile healthcare applications

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches.

/pdf/wearables-in-medicine-5azfm8fp5w.pdf

Wearables in Medicine

Clustering has been proven to be one of the most efficient techniques for saving energy of wireless sensor networks (WSNs). However, in a hierarchical cluster based WSN, cluster heads (CHs) consume more energy due to extra overload for receiving and aggregating the data from their member sensor nodes and transmitting the aggregated data to the base station. Therefore, the proper selection of CHs plays vital role to conserve the energy of sensor nodes for prolonging the lifetime of WSNs. In this paper, we propose an energy efficient cluster head selection algorithm which is based on particle swarm optimization (PSO) called PSO-ECHS. The algorithm is developed with an efficient scheme of particle encoding and fitness function. For the energy efficiency of the proposed PSO approach, we consider various parameters such as intra-cluster distance, sink distance and residual energy of sensor nodes. We also present cluster formation in which non-cluster head sensor nodes join their CHs based on derived weight function. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The results are compared with some existing algorithms to demonstrate the superiority of the proposed algorithm.

A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks

Wireless sensor networks (WSNs) can be used by the military for a number of purposes such as monitoring or tracking the enemies and force protection. Unlike commercial WSNs, a tactical military sensor network has different priority requirements for military usage. Especially in the remote large-scale network, topology, self-configuration, network connectivity, maintenance, and energy consumption are the challenges. In this paper, we present an overview of application scenarios in remote large-scale WSNs focusing on the primary requirements for tactical environments. We propose a sensor network architecture based on the cluster-tree based multi-hop model with optimized cluster head election and the corresponding node design method to meet the tactical requirements. With the proposed WSN architecture, one can easily design the sensor network for military usage in remote large scale environments.

Wireless sensor network design for tactical military applications : Remote large-scale environments

Virtual reality (VR)-based rehabilitation has been reported to have beneficial effects on upper extremity function in stroke survivors; however, there is limited information about its effects on distal upper extremity function and health-related quality of life (HRQoL). The purpose of the present study was to examine the effects of VR-based rehabilitation combined with standard occupational therapy on distal upper extremity function and HRQoL, and compare the findings to those of amount-matched conventional rehabilitation in stroke survivors. The present study was a single-blinded, randomized controlled trial. The study included 46 stroke survivors who were randomized to a Smart Glove (SG) group or a conventional intervention (CON) group. In both groups, the interventions were targeted to the distal upper extremity and standard occupational therapy was administered. The primary outcome was the change in the Fugl–Meyer assessment (FM) scores, and the secondary outcomes were the changes in the Jebsen–Taylor hand function test (JTT), Purdue pegboard test, and Stroke Impact Scale (SIS) version 3.0 scores. The outcomes were assessed before the intervention, in the middle of the intervention, immediately after the intervention, and 1 month after the intervention. The improvements in the FM (FM-total, FM-prox, and FM-dist), JTT (JTT-total and JTT-gross), and SIS (composite and overall SIS, SIS-social participation, and SIS-mobility) scores were significantly greater in the SG group than in the CON group. VR-based rehabilitation combined with standard occupational therapy might be more effective than amount-matched conventional rehabilitation for improving distal upper extremity function and HRQoL. This study is registered under the title “Effects of Novel Game Rehabilitation System on Upper Extremity Function of Patients With Stroke” and can be located in https://clinicaltrials.gov with the study identifier NCT02029651
 .

/pdf/effects-of-virtual-reality-based-rehabilitation-on-distal-4mc6wx98p4.pdf

Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial

Convergecast, which is frequently used in wireless sensor network applications, causes what is commonly called the {energy hole problem}. Aggregating data using spatial correlation in cluster-based networks is one way of solving this problem. In this letter, we derive the upper bound on the network lifetime in cluster-based sensor networks. Then, we investigate the effects of the number of clusters and spatial correlation on the network lifetime bound.

Analysis of Network Lifetime in Cluster-Based Sensor Networks

The large-scale deployment of high density wireless sensor networks (WSNs) necessitates efficient organization of the networks for high network connectivity and low orphan node ratio. The multi-hop clustering technique has proven to be an efficient one for self-organizing thousands of randomly deployed sensors in a huge area. In this paper, we propose new clustering algorithms with a set of parameters for clustering nodes in static ad hoc sensor networks. First, we present a gradual cluster head election algorithm (GCA) that gradually elects cluster heads according to the proximity to neighbor nodes and the residual energy level (E r ) at each clustering phase. In addition, we propose a gradual cluster head election algorithm with one-hop neighbor information (GCA-ON), which elects cluster heads based on E r and the relative location information of sensor nodes such as the number of cluster heads in one-hop neighbor (N CH-ON ). Ideal distributions or capabilities of the sensor nodes are not assumed in the proposed algorithms. We evaluate the performance of the proposed algorithms in large-scale high density WSN environments. In view of network connectivity, our algorithms outperform the typical existing cluster head election algorithms.

Gradual cluster head election for high network connectivity in large-scale sensor networks

Disclosed is a rehabilitation training apparatus. The rehabilitation training apparatus includes a pair of first tracks that are arranged in parallel at an interval, a second track movably connected to the pair of first tracks, a hand holder that is movably provided in the second track and on which a hand and an arm of a user is held, and a stopper for a track provided in at least one of the first tracks and the second track to limit movements of the second track and the hand holder.

Soobin Lee

Papers

Wireless sensor network design for tactical military applications : Remote large-scale environments

Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial

Analysis of Network Lifetime in Cluster-Based Sensor Networks

Gradual cluster head election for high network connectivity in large-scale sensor networks

Rehabilitation training apparatus