Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson’s Disease

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor®, a medical device for long-term Parkinson’s disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson’s disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

A MAC PROTOCOL FOR ENERGY EFFICIENCY AND FAST EMERGENCY MEDICAL JUDGMENT IN WIRELESS BODY SENSOR NETWORKS

Emergency data collected from sensor nodes widely distributed in wireless body sensor networks (WBSNs) are delivered to medical staff as quickly as possible, so patients’ lives can be saved through appropriate actions and treatments. However, relevant data and vital data may be required for appropriate actions by the medical staff. Therefore, all these data must be properly delivered to the medical staff within the set time. In this paper, we propose a MAC protocol with a reservation function and an operation frame extension function to extend the overall network lifetime by reducing the energy consumption of given sensor nodes and quickly deliver information to medical systems in case of emergency. This MAC protocol makes it possible to achieve fast transmission of related data by utilizing the related-priority slots. As a result of the experiment, the transmission delay was reduced by about 12.5%, and the lifetime was increased by approximately 19% over the existing MAC protocol. It also can be seen that the proposed MAC protocol works well in an environment where emergency events often occur.

Electrospun bundled carbon nanofibers for skin-inspired tactile sensing, proprioception and gesture tracking applications

In this work, we report a class of wearable, stitchable, and sensitive carbon nanofiber (CNF)-polydimethylsiloxane (PDMS) composite-based piezoresistive sensors realized by carbonizing electrospun polyacrylonitrile (PAN) nanofibers and subsequently embedding in PDMS elastomeric thin films. Electro-mechanical tactile sensing characterization of the resulting piezoresistive strain sensors revealed a linear response with an average force sensitivity of ~1.82 kN−1 for normal forces up to 20 N. The real-time functionality of the CNF-PDMS composite sensors in wearable body sensor networks and advanced bionic skin applications was demonstrated through human motion and gesture monitoring experiments. A skin-inspired artificial soft sensor capable of demonstrating proprioceptive and tactile sensory perception utilizing CNF bundles has been shown. Furthermore, a 16-point pressure-sensitive flexible sensor array mimicking slow adapting low threshold mechanoreceptors of glabrous skin was demonstrated. Such devices in tandem with neuromorphic circuits can potentially recreate the sense of touch in robotic arms and restore somatosensory perception in amputees.

A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds

A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.