Periodical monitoring of renal function, specifically for subjects with history of diabetic or hypertension would prevent them from entering into chronic kidney disease (CKD) condition. The recent increase in numbers may be due to food habits or lack of physical exercise, necessitates a rapid kidney function monitoring system. Presently, it is determined by evaluating glomerular filtration rate (GFR) that is mainly dependent on serum creatinine value and demographic parameters and ethnic value. Attempted here is to develop ethnic parameter based on skin texture for every individual. This value when used in GFR computation, the results are much agreeable with GFR obtained through standard modification of diet in renal disease and CKD epidemiology collaboration equations. Once correlation between CKD and skin texture is established, classification tool using artificial neural network is built to categorise CKD level based on demographic values and parameter obtained through skin texture (without using creatinine). This network when tested gives almost at par results with the network that is trained with demographic and creatinine values. The results of this Letter demonstrate the possibility of non-invasively determining kidney function and hence for making a device that would readily assess the kidney function even at home.

Wireless medical sensor network is used in healthcare applications that have the collections of biosensors connected to a human body or emergency care unit to monitor the patient's physiological vital status. The real-time medical data collected using wearable medical sensors are transmitted to a diagnostic centre. The data generated from the sensors are aggregated at this centre and transmitted further to the doctor's personal digital assistant for diagnosis. The unauthorised access of one's health data may lead to misuse and legal complications while unreliable data transmission or storage may lead to life threatening risk to patients. So, this Letter combines the symmetric algorithm and attribute-based encryption to secure the data transmission and access control system for medical sensor network. In this work, existing systems and their algorithm are compared for identifying the best performance. The work also shows the graphical comparison of encryption time, decryption time and total computation time of the existing and the proposed systems.

The healthcare industries research trends focus on patient reliable communication and security is a paramount requirement of healthcare applications. Jamming in wireless communication medium has become a major research issue due to the ease of blocking communication in wireless networks and throughput degradation. The most commonly used technique to overcome jamming is frequency hopping (FH). However, in traditional FH pre-sharing of key for channel selection and a high-throughput overhead is required. So to overcome this pre-sharing of key and to increase the security chaotic frequency hopping (CFH) has been proposed. The design of chaos-based hop selection is a new development that offers improved performance in transmission of information without pre-shared key and also increases the security. The authors analysed the performance of proposed CFH system under different reactive jamming durations. The percentage of error reduction by the reactive jamming for jamming duration 0.01 and 0.05 s for FH and CFH is 55.03 and 84.24%, respectively. The obtained result shows that CFH is more secure and difficult to jam by the reactive jammer.

Anxiety and depression are two important mental health problems among the geriatric population. They are often undiagnosed and directly or indirectly responsible for various morbidities. Early and timely diagnosis has immense effect on appropriate management of anxiety and depression along with its co-morbidities. Owing to time constraint and enormous patient load, especially in developing county such as India it is hardly possible for a physician or surgeon to identify a geriatric patient suffering from anxiety and depression using any psychometric analysis tool. So, it is of utmost importance to develop a predictive model for automated diagnosis of anxiety and depression among them. This Letter aims to develop an appropriate predictive model, to diagnose anxiety and depression among older patient from socio-demographic and health-related factors, using machine learning technology. Ten classifiers were evaluated with a data set of 510 geriatric patients and tested with ten-fold cross-validation method. Highest prediction accuracy of 89% was obtained with random forest (RF) classifier. This RF model was tested with another data set from separate 110 older patients for its external validity. Its predictive accuracy was found to be 91% and false positive (FP) rate was 10%, compared with gold standard tool.

In our daily life, inadvertent scratching may increase the severity of skin diseases (such asatopic dermatitis etc.). However, people rarely pay attention to this matter, so the knownmeasurement behaviour of the movement is also very little. Nevertheless, the behaviour and frequencyof scratching represent the degree of itching, and the analysis of scratching frequency is helpfulto the doctor's clinical dosage. In this Letter, a novel system is proposed to monitor thescratching motion of a sleeping human body at night. The core device of the system is just a leakycoaxial cable (LCX) and a router. Commonly, LCX is used in the blind field or semi-blindfield inwireless communication. The new idea is that the leaky cable is placed on the bed, and then thestate information of physical layer of wireless communication channels is acquired to identify thescratching motion and other small body movements in the human sleep process. The results show thatit can be used to detect the movement and its duration. Channel state information (CSI) packet iscollected by card installed in the computer based on the 802.11n protocol. The characterisation ofthe scratch motion in the collected CSI is unique, so it can be distinguished from the wirelesschannel amplitude variation trend.