Fault-Tolerant and Low-Power Sampling Schedules for Localized BASNs

Abstract

Abstract-Recent advances in the scope of wearable devices and networks make body area sensor networks (BASNs) an extremely attractive tool to the fields of mobile and tele-health, owing to the range of medical applications they can serve and the diagnostic richness of patient data they can offer. However, for BASNs to achieve true ubiquity, they must be scalable in their support of automated patient data collection, making usability and reliability key considerations. Its designers must wrestle with the tradeoff between usability, hindered by device intrusiveness into the behaviors it measures, and lifetime, enhanced by large power supplies and expensive, sturdy components. Furthermore, the validity and reliability of the collected data are paramount. In this paper, we consider these issues in the context of localized multi-sensory wearable networks and present a method to generate low-power sampling schedules that are resilient to sensor faults while achieving high diagnostic fidelity. We jointly formulate this as a power-constrained sampling problem wherein the number of sensors sampled per epoch are limited, and, a fault tolerant scheduling problem wherein the sampling scheme offers enough redundancy to endure up to a predefined number of sensor faults while maintaining diagnostic accuracy. This formulation is based on, 1) the localized scope of BASNs that engenders strong spatio-temporal interactions in the samples, and, 2) the periodic nature of human behaviors measured. We present our algorithm in the context of gait diagnostics derived from a foot plantar pressure measurement platform and illustrate its performance based on real datasets collected by it.Index Terms-Body area sensor networks, energy-efficient sampling, fault-tolerant sampling, power-constrained sampling.

Design and Implementation of Wearable Device for Neuropathic Diabetic Foot Patients

“…These systems are elastic and portable which allows it a larger variety of studies with different gait analysis tasks, footwear designs, etc. Hence they are highly suggested for studying footwear design [4], [7], [10], [11]. But there are some disadvantages like the possibility of the sensor slipping; the spatial declaration of the data is low as compared to platform based systems due to smaller amount of sensors.…”

“…The author Vishwa Goudar and Miodrag Potkonjak proposed CICA based algorithm which was related to gait determination resultant from a foot plantar pressure measurement platform and present a method for generating the low power sampling schedules that are useful for the sensor faults while archiving high diagnostic fidelity [4].…”

On Admitting Sensor Fault Tolerance While Achieving Secure Biosignal Data Sharing

“…Often, fault-tolerant designs involve redundancy, wherein the coverage "area" is spanned by more sensors than are necessary, which enables the system to reconstruct the spatio-temporal signal profile of the entire coverage area from a functioning subset of sensors when others report faulty measurements. For example, sensor selection approaches have been proposed [6] wherein a sensor subset are constructed based on their ability to jointly predict measurements at all sensors. In this paper we take a similar approach towards fault tolerance with the construction of multiple redundant subsets such that the semantics of the entire spatio-temporal signal may be reconstructed accurately and indepedently from the individual subsets, while the subsets simultaneously admit effective and independent embedding of watermarks in data generated by each of the subsets.…”

“…We shall study the performance of our watermarking technique in the context of a gait stability monitoring BASN called HERMES [7][8] [6] [9]. HERMES is a smart-shoe composed of 99 passive-resistive pressure sensors located across the shoe insole, that are used to monitor foot plantar pressure as the subject ambulates.…”

Addressing biosignal data sharing security issues with robust watermarking

“…However, several requirements are still being tackled towards successful large-scale and long-term adoption of BASNs. These include improvements in the design and configuration of existing systems so they may afford as yet unachieved capabilities and improved performance, spanning improved sensing, signal processing, communication, data storage and power-management [1] [2][3] [4][5] [6], as well as, enhanced infrastructure support for the deployed BASNs and management of the data they collect.…”