Exploring the Importance and Performance Priorities of Older Adults With a User-Centred Approach to Create a Fall-Free Bathroom.

Background: Fall hazards in bathroom spaces constitute one of the most critical issues in the daily lives of older adults. Bathroom falls are somewhat different and constrained in nature than those in other parts of a home environment.

Objectives: This study aimed to adopt a user-centred approach to explore older adults' general bathroom needs, with a specific focus on showers and bathtubs as the designated activity area.

Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions.

Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations.

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data.

Localization and Tracking of Implantable Biomedical Sensors.

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment.

Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals.

We develop an autonomous system to detect and evaluate physical therapy exercises using wearable motion sensors. We propose the multi-template multi-match dynamic time warping (MTMM-DTW) algorithm as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session. While allowing some distortion (warping) in time, the algorithm provides a quantitative measure of similarity between an exercise execution and previously recorded templates, based on DTW distance.

Detecting falls with wearable sensors using machine learning techniques.

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass).

Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals.

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them.

Classifying human leg motions with uniaxial piezoelectric gyroscopes.

This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN).

Objective Error Criterion for Evaluation of Mapping Accuracy Based on Sensor Time-of-Flight Measurements.

An objective error criterion is proposed for evaluating the accuracy of maps of unknown environments acquired by making range measurements with different sensing modalities and processing them with different techniques. The criterion can also be used for the assessment of goodness of fit of curves or shapes fitted to map points. A demonstrative example from ultrasonic mapping is given based on experimentally acquired time-of-flight measurements and compared with a very accurate laser map, considered as absolute reference.

Complex signal recovery from multiple fractional Fourier-transform intensities.

The problem of recovering a complex signal from the magnitudes of any number of its fractional Fourier transforms at any set of fractional orders is addressed. This problem corresponds to the problem of phase retrieval from the transverse intensity profiles of an optical field at arbitrary locations in an optical system involving arbitrary concatenations of lenses and sections of free space. The dependence of the results on the number of orders, their spread, and the noise is investigated.

Fractional Fourier transform pre-processing for neural networks and its application to object recognition.

This study investigates fractional Fourier transform pre-processing of input signals to neural networks. The fractional Fourier transform is a generalization of the ordinary Fourier transform with an order parameter a. Judicious choice of this parameter can lead to overall improvement of the neural network performance.