Everyone has a different way of walking, and for this reason, gait has been studied in the last few years as an important biometric information source. This study explores a novel approach, based on ultra-wideband (UWB) technology, for user identification via gait analysis. In the proposed method, the user is supposed to wear two or more devices embedding a UWB transceiver. During gait, the distances between the devices are estimated via UWB and then analysed by means of a machine learning classifier, which provides automatic identification. Experiments were carried out by 12 volunteers, who walked while wearing four UWB boards (placed on the head, wrist, ankle, and in a trouser pocket). The off-line evaluation considered a set of different possible configurations in terms of number and position of the wearable devices. Despite a relatively low sampling frequency of 10 Hz, the results are promising: average identification accuracy is as high as ∼96% with four devices, and above 90% with three devices (wrist, trouser pocket, and ankle). This novel approach may enhance the accuracy of inertial-based systems for continuous user identification.

The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.

Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.

Oxygen loaded microbubbles are being investigated as a means of reducing tumour hypoxia in order to improve response to cancer therapy. To optimise this approach, it is desirable to be able to measure changes in tissue oxygenation in real-time during treatment. In this study, the feasibility of using magnetic resonance imaging (MRI) for this purpose was investigated. Longitudinal relaxation time (T1) measurements were made in simple hydrogel phantoms containing two different concentrations of oxygen microbubbles. T1 was found to be unaffected by the presence of oxygen microbubbles at either concentration. Upon application of ultrasound to destroy the microbubbles, however, a statistically significant reduction in T1 was seen for the higher microbubble concentration. Further work is needed to assess the influence of physiological conditions upon the measurements, but these preliminary results suggest that MRI could provide a method for quantifying the changes in tissue oxygenation produced by microbubbles during therapy.

The performance of an acoustic stethoscope is improved by translating, without loss of fidelity, heart sounds, chest sounds, and intestinal sounds below 50 Hz into a frequency range of 200 Hz, which is easily detectable by the human ear. Such a frequency translation will be of significant benefit to hearing impaired physicians and it will improve the stethoscope performance in a noisy environment. The technique is based on a single sideband suppressed carrier modulation. Stability and bias problems commonly associated with an analog frequency translator are avoided by an all-digital implementation. Real-time audio processing is made possible by approximating a Hilbert transformer with a time delay. The performance of the digital frequency translator was verified with a 16-bit 44.1 Ks/s audio coder/decoder and a 32-bit 72 MHz microcontroller.

Venous leg ulcerations are a common problem, with high prevalence in the middle-aged and elderly population, and more attention on research of their physical activities has been paid, as they have great effects on the blood circulation of the lower limb. With enough, appropriate training, the chronic venous ulcerations in the lower limb can be avoided and alleviated, and venous hypertension can be reduced effectively. The study deals with a physical activity tracking system for the patients based on a three-axis accelerometer. The system uses a three-axis accelerometer, a microcontroller, and a wireless Bluetooth module to form a data acquisition platform to acquire accelerations of the lower limb movement, and sends it to a smart mobile phone via the wireless Bluetooth module. The system takes advantages of the smart mobile phone to guide the chronic venous leg ulcers to do prescribed rehabilitation exercises for the lower limb muscles, perform acceleration data preprocessing, wavelet transform and reconstruction, denoising and feature extraction, obtain the results of the rehabilitation exercises, and then give reasonable evaluation and judgment. It is helpful to treat underlying venous reflux, create such an environment that allows skin to grow across an ulcer, and accelerate ulcer healing process consequently.