An early warning system for diabetic automobile drivers with peripheral neuropathy.

PURPOSE: People with Type 2 diabetes exhibit peripheral neuropathy that results in the progressive loss of sensation in their feet. This may adversely affect their ability to drive as there is the potential for their foot to slip off the accelerator or brake pedals, with unwanted consequences including traffic accidents. This research aimed to develop a prototype for an adaptive haptic foot device for diabetic drivers experiencing peripheral neuropathy that can serve as an early warning system for foot slip during driving. METHODS: A prototype system was designed in the laboratory which consisted of four force sensing resistors, four light emitting diodes and an eccentric rotating mass all connected and programmed through an Arduino Uno. The prototype was tested under controlled conditions and validated against recommended specifications. The system was then installed in a Ford Falcon GT 2005 and tested under controlled road conditions. RESULTS: The results indicated that the haptic device was effective in sensing foot locations and providing instant audio and video feedback to the driver. CONCLUSION: This research has successfully designed and fabricated a haptic feedback device that can be used as an early warning system for diabetic automobile drivers with peripheral neuropathy.IMPLICATIONS FOR REHABILITATIONA haptic foot prototype device capable of generating warning signals to diabetic drivers whose foot could slip off the brake or accelerator pedals has been developed.The prototype includes force sensing receivers integrated with eccentric rotating mass system, a haptic controller breakout board, and Arduino software.The system is very easy to use and provides highly reliable audio and visual feedback which are good alerting mechanisms for older automobile drivers.

Gender dependant snore sound based multi feature obstructive sleep apnea screening method.

Obstructive Sleep Apnea (OSA) is a serious sleep disorder that occurs due to collapsing upper airways (UA). More than 80% of OSA sufferers remain undiagnosed and the situation demands simplified, convenient technology for community screening. Almost all OSA patients snore and snoring is the earliest nocturnal symptom of OSA. Snore signals (SS) are produced due to vibration of soft tissues in the narrowed parts of the UA. It is known that the UA properties are gender specific. In this paper, we work under the hypothesis that gender specific analysis of snore sounds should lead to a higher OSA detection performance. We propose a snore based multi-parametric OSA screening technique, which incorporates the gender differences in the algorithm. The multi feature vector was modeled using logistic regression based algorithms to classify subjects into OSA/non-OSA classes. The performance of the proposed method was evaluated by carrying out K-fold cross validation. This procedure was applied to male (n=51) and female (n=36) data sets recorded in a clinical sleep laboratory. Each data set consisted of sound recordings of 6-8 hr. duration. The performance of the method was evaluated against the standard laboratory method of diagnosis known as polysomongraphy. Our gender-specific technique resulted in a sensitivity of 93±9% with specificity 89±7% for females and sensitivity of 91±8% with specificity 89±12% for males. These results establish the possibility of developing cheap, convenient, non-contact and an unattended OSA screening technique.