An Intelligent Homecare Emergency Service System for Elder Falling

[[abstract]]As aging people is growing quickly in many countries, the fall problem is formed a curial public health and clinical problem among elderly persons because fall is the prime cause for traumatic death and physical sequela of them. However, as many of the elders choose solitary life alone and because of the isolation, the emergency service model has faced two main challenges: first, how to discover the fall accident; and next, how to communicate the emergency service center. In this research, we propose an intelligent homecare emergency service system based on two intelligent technologies: artificial neural network and intelligent software agent. The fall detector that based on tri-axes accelerometer and back-propagation neural network classifier is implemented to detect the fall events automatically. On the other hand, an intelligent agent-based homecare emergency service system is developed to communicate the emergency service center and hospitals for requesting help. In the meantime, the basic and important health information related to the elder will be sent together with that request. Thus, the emergency service center can base on the elder’s information to dispatch an available ambulance that takes the necessary medicines and equipment to the elder’s house

An Enhanced Lightweight Dynamic Pseudonym Identity Based Authentication and Key Agreement Scheme Using Wireless Sensor Networks for Agriculture Monitoring

Agriculture plays an important role for many countries. It provides raw materials for foodand provides large employment opportunities for people in the country, especially for countrieswith a dense population. To enhance agriculture productivity, modern technology such as wirelesssensor networks (WSNs) can be utilized to help in monitoring important parameters in thwagricultural field such as temperature, light, soil moisture, etc. During the monitoring process, ifsecurity compromises happen, such as interception or modification of the parameters, it may leadto false decisions and bring damage to agriculture productivity. Therefore, it is very important todevelop secure authentication and key agreement for the system. Recently, Ali et al. proposed anauthentication and key agreement scheme using WSNs for agriculture monitoring. However, it failsto provide user untraceability, user anonymity, and session key security; it suffers from sensor nodeimpersonation attack and perfect forward secrecy attack; and even worse has denial of service as aservice. This study discusses these limitations and proposes a new secure and more efficientauthentication and key agreement scheme for agriculture monitoring using WSNs. The proposedscheme utilizes dynamic pseudonym identity to guarantee user privacy and eliminates redundantcomputations to enhance efficiency

Predicting Recovery of Voluntary Upper Extremity Movement in Subacute Stroke Patients with Severe Upper Extremity Paresis

<div><p>Background and Objective</p><p>Prediction of voluntary upper extremity (UE) movement recovery is largely unknown in patients with little voluntary UE movement at admission. The present study aimed to investigate (1) the extent and variation of voluntary UE movement recovery, and (2) the best predictive model of the recovery of voluntary UE movement by clinical variables in patients with severe UE paresis.</p><p>Design</p><p>Prospective cohort study.</p><p>Methods</p><p>140 (out of 590) stroke patients with severe UE paresis completed all assessments. Voluntary UE movement was assessed using the UE subscale of the Stroke Rehabilitation Assessment of Movement (STREAM-UE). Two outcome measures, STREAM-UE scores at discharge (DC_STREAM-UE) and changes between admission and discharge (Δ_STREAM-UE), were investigated to represent the final states and improvement of the recovery of voluntary UE movement. Stepwise regression analyses were used to investigate 19 clinical variables and to find the best predictive models of the two outcome measures.</p><p>Results</p><p>The participants showed wide variation in both DC_STREAM-UE and Δ_STREAM-UE. 3.6% of the participants almost fully recovered at discharge (DC_STREAM-UE > 15). A large improvement (Δ_STREAM-UE >= 10) occurred in 16.4% of the participants, while 32.9% of the participants did not have any improvement. The four predictors for the DC_STREAM-UE (R² = 35.0%) were ‘baseline STREAM-UE score’, ‘hemorrhagic stroke’, ‘baseline National Institutes of Health Stroke Scale (NIHSS) score’, and ‘cortical lesion excluding primary motor cortex’. The three predictors for the Δ_STREAM-UE (R² = 22.0%) were ‘hemorrhagic stroke’, ‘baseline NIHSS score’, and ‘cortical lesion excluding primary motor cortex’.</p><p>Conclusions</p><p>Recovery of voluntary UE movement varied widely in patients with severe UE paresis after stroke. The predictive power of clinical variables was poor. Both results indicate the complex nature of voluntary UE movement recovery in patients with severe UE paresis after stroke.</p></div

The patient enrolment flow diagram.

<p>The patient enrolment flow diagram.</p

The scatter plots of baseline STREAM-UE scores versus STREAM-UE DC scores (A) and STREAM-UE change scores (B). STREAM-UE = the UE subscale of the Stroke Rehabilitation Assessment of Movement measure. DC = discharge.

<p>The scatter plots of baseline STREAM-UE scores versus STREAM-UE DC scores (A) and STREAM-UE change scores (B). STREAM-UE = the UE subscale of the Stroke Rehabilitation Assessment of Movement measure. DC = discharge.</p

Multiple regression analysis for the DC_STREAM-UE and Δ_STREAM-UE (n = 140).

<p>ΔSTREAM-UE = change score of STREAM-UE</p><p>^a. Predictors: (Constant), Baseline STREAM-UE score</p><p>^b. Predictors: (Constant), Baseline STREAM-UE score, Hemorrhagic stroke</p><p>^c. Predictors: (Constant), Baseline STREAM-UE score, Hemorrhagic stroke, Baseline NIHSS score</p><p>^d. Predictors: (Constant), Baseline STREAM-UE score, Hemorrhagic stroke, Baseline NIHSS score, Cortical-primary motor cortex uninvolved</p><p>^e. Predictors: (Constant), Hemorrhagic stroke</p><p>^f. Predictors: (Constant), Hemorrhagic stroke, Baseline NIHSS score</p><p>^g. Predictors: (Constant), Hemorrhagic stroke, Baseline NIHSS score, Cortical-primary motor cortex uninvolved</p><p>Multiple regression analysis for the DC_STREAM-UE and Δ_STREAM-UE (n = 140).</p

Univariate analysis for the STREAM-UE change scores (Δ_STREAM-UE) (n = 140).

<p>STREAM = Stroke Rehabilitation Assessment of Movement.</p><p>NIHSS = National Institutes of Health Stroke Scale</p><p>* Variables with p value < 0.1 and were put in the regression model for selection</p><p>^† Shoulder abduction and finger extension scores were obtained from the STREAM-UE scale</p><p>Univariate analysis for the STREAM-UE change scores (Δ_STREAM-UE) (n = 140).</p