Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey.

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context.

Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring.

Traditional pedobarography methods use direct force sensor placement in the shoe insole to record pressure patterns. One problem with such methods is that they tap only a few points on the flat sole under the foot and, therefore, do not account for the total ground reaction force. As a result, body weight tends to be under-estimated.

A Self-Synchronizing, Low-Power, Low-Complexity Transceiver for Body-Coupled Communication.

This paper presents a self-synchronizing, low-power, low-complexity body-coupled communication (BCC) transceiver using the recently proposed Pulsed-Index Communication (PIC) techniques. The unique features of these techniques are used to simplify the BCC transceiver hardware and reduce its power consumption by eliminating the need for circuitries dedicated to clock and data recovery (CDR) and duty cycle correction. The self-synchronizing feature of the transceiver is achieved by exploiting the edge-coding property of PIC which consists of using pulse edges for encoding and detecting transmitted pulses rather than bit times or duty cycles.