Development of a novel evidence-based automated powered mobility device competency assessment.

This paper describes the outcomes of a clinical study to assess the validity of a stand-alone sensor package and algorithms to aid the assessment by an occupational therapist (OT) whether a person has the capacity to safely and effectively operate a powered mobility device such as a wheelchair in their daily activities. The proposed solution consists of a suite of sensors capable of inferring navigational characteristics from the platform it is attached to (e.g. trajectories, map of surroundings, speeds, distance to doors, etc). Such information presents occupational therapists with the ability to augment their own observations and assessments with correlated, quantitative, evidence-based data acquired with the sensor array. Furthermore, OT reviews can take place at the therapist's discretion as the data from the trials is logged. Results from a clinical evaluation of the proposed approach, taking as reference the commonly-used Power-Mobility Indoor Driving Assessment (PIDA) assessment, were conducted at the premises of the Prince of Wales (PoW) Hospital in Sydney by four users, showing consistency with the OT scores, and setting the scene to a larger study with wider targeted participation.

Muscle computer interfaces for driver distraction reduction.

Driver distraction is regarded as a significant contributor to motor-vehicle crashes. One of the important factors contributing to driver distraction was reported to be the handling and reaching of in-car electronic equipment and controls that usually requires taking the drivers' hands off the wheel and eyes off the road. To minimize the amount of such distraction, we present a new control scheme that senses and decodes the human muscles signals, denoted as Electromyogram (EMG), associated with different fingers postures/pressures, and map that to different commands to control external equipment, without taking hands off the wheel. To facilitate such a scheme, the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different EMG-based actions and to do so in a computationally efficient manner. In this paper, an accurate and efficient method based on Fuzzy Neighborhood Discriminant Analysis (FNDA), is proposed for discriminant feature extraction and then extended to the channel selection problem. Unlike existing methods, the objective of the proposed FNDA is to preserve the local geometrical and discriminant structures, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA by employing the QR-decomposition. Practical real-time experiments with eight EMG sensors attached on the human forearm of eight subjects indicated that up to fourteen classes of fingers postures/pressures can be classified with <7% error on average, proving the significance of the proposed method.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.