Ultra-portable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness.

We demonstrate a smartphone based spectrometer design that is standalone and supported on a wireless platform. The device is inherently low-cost and the power consumption is minimal making it portable to carry out a range of studies in the field. All essential components of the device like the light source, spectrometer, filters, microcontroller and wireless circuits have been assembled in a housing of dimensions 88 mm × 37 mm × 22 mm and the entire device weighs 48 g. The resolution of the spectrometer is 15 nm, delivering accurate and repeatable measurements. The device has a dedicated app interface on the smartphone to communicate, receive, plot and analyze spectral data. The performance of the smartphone spectrometer is comparable to existing bench-top spectrometers in terms of stability and wavelength resolution. Validations of the device were carried out by demonstrating non-destructive ripeness testing in fruit samples. Ultra-Violet (UV) fluorescence from Chlorophyll present in the skin was measured across various apple varieties during the ripening process and correlated with destructive firmness tests. A satisfactory agreement was observed between ripeness and fluorescence signals. This demonstration is a step towards possible consumer, bio-sensing and diagnostic applications that can be carried out in a rapid manner.

Design and preliminary evaluation of a wearable device for mass-screening of sleep apnea.

Between 7-18 million Americans suffer from sleep disordered breathing (SDB), including those who suffer from obstructive sleep apnea (OSA). Despite this high prevalence and burden of OSA, existing diagnostic techniques remain impractical for widespread screening. In this study, we introduce a new model for OSA screening and describe an at-home wearable sleep mask (named ARAM) that can robustly track the wearers' sleep patterns. This monitoring is achieved using select sensors that enable screening and monitoring in a form-factor that can be easily self-instrumented. Based on feedback from sleep doctors and technicians, we incorporate the most valuable sensors for OSA diagnosis, while maintaining ease-of-use and comfort for the patient. We discuss the results of preliminary field trials, where both our sleep mask and a commercially available device were worn simultaneously to evaluate our device's robustness. Based on these results, we discuss next steps for the design of the screening system, including analyses techniques that would provide more efficient screening than existing systems.

Psycho-physiological training approach for amputee rehabilitation.

Electromyography (EMG) signals are very noisy and difficult to acquire. Conventional techniques involve amplification and filtering through analog circuits, which makes the system very unstable. The surface EMG signals lie in the frequency range of 6Hz to 600Hz, and the dominant range is between the ranges from 20Hz to 150Hz. 1 Our project aimed to analyze an EMG signal effectively over its complete frequency range. To remove these defects, we designed what we think is an easy, effective, and reliable signal processing technique. We did spectrum analysis, so as to perform all the processing such as amplification, filtering, and thresholding on an Arduino Uno board, hence removing the need for analog amplifiers and filtering circuits, which have stability issues. The conversion of time domain to frequency domain of any signal gives a detailed data of the signal set. Our main aim is to use this useful data for an alternative methodology for rehabilitation called a psychophysiological approach to rehabilitation in prosthesis, which can reduce the cost of the myoelectric arm, as well as increase its efficiency. This method allows the user to gain control over their muscle sets in a less stressful environment. Further, we also have described how our approach is viable and can benefit the rehabilitation process. We used our DSP EMG signals to play an online game and showed how this approach can be used in rehabilitation.