Augmentative and alternative communication (AAC) advances: A review of configurations for individuals with a speech disability

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications

Breathing pattern interpretation as an alternative and effective voice communication solution

Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the outside world by means of a unidirectional microphone and researches breathing-pattern interpretation (BPI) to encode messages in an interactive manner with minimal training. We present BP processing work with (1) output synthesized machine-spoken words (SMSW) along with single-channel Weiner filtering (WF) for signal de-noising, and (2) k-nearest neighbor (k-NN) classification of BPs associated with embedded dynamic time warping (DTW). An approved protocol to collect analogue modulated BP sets belonging to 4 distinct classes with 10 training BPs per class and 5 live BPs per class was implemented with 23 healthy subjects. An 86% accuracy of k-NN classification was obtained with decreasing error rates of 17%, 14%, and 11% for the live classifications of classes 2, 3, and 4, respectively. The results express a systematic reliability of 89% with increased familiarity. The outcomes from the current AAC setup recommend a durable engineering solution directly beneficial to the sufferers

Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Photoplethysmography (PPG) based pulse oximetry devices normally use red and infrared illuminations to obtain oxygen saturation (SpO2) readings. In addition, the presence of motion artefacts severely restricts the utility of pulse oximetry physiological measurements. In the current study, a combination of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) was investigated in order to improve robustness to subjects’ movements in the extraction of SpO2 measurement. The experimental protocol with 31 healthy subjects was divided into two sub-protocols, and was designed to determine SpO2 measurement. The datasets for the first sub-protocol were collected from 15 subjects at rest, with the subjects free to move their hands. The datasets for the second sub-protocol with 16 subjects were collected during cycling and walking exercises. The results showed good agreement with SpO2 measurements (r = 0.98) in both sub-protocols. The outcomes promise a robust and cost-effective approach of physiological monitoring with the prospect of providing health monitoring that does not restrict user physical movements

Interpretation of human breathing patterns towards a new augmentative and alternative communication solution

Interpretation of human breathing patterns towards a new augmentative and alternative communication solutio

A study of decodable breathing patterns for augmentative and alternative communication

People who use high-tech augmentative and alternative communication (AAC) solutions still face restrictions in terms of practical utilization of present AAC devices, especially when speech impairment is compounded with motor disabilities. This study aims to explore an effective way to decode breathing patterns for AAC by the means of a breath activated dynamic air pressure detection system (DAPDS) and supervised machine learning (ML). The aim is to detect a user’s modulated breathing patterns (MBPs) and turn them into synthesized messages forconversation with the outside world. MBPs are processed using a one-nearest neighbor (1-NN) algorithm with variations of dynamic time warping (DTW) to produce synthesized machine spoken words (SMSW) at managed complexities and speeds. An ethical approved protocol was conducted with the participation of 25 healthy subjects to create a library of 1500 MBPs corresponding to four different classes. A mean systematic classification accuracy of 91.97 % was obtained using the current configuration. The implications from the study indicate that an improved AAC solution and speaking biometrics decoding could be undertaken in the future. </div