Technology and Health Care

Assistive Technology (AT) has been utilized to support people with dementia (PwD) and their carers in the home. Such support can extend the time that PwD can remain safely at home and reduce the burden on the tertiary healthcare sector. Technology can assist people in the hours of darkness as well as during the day. The objective of this literature review is to evaluate reported healthcare technologies appropriate to night time care. This paper summarises and categorises the current evidence base. In all, 131 abstracts were returned from a database search, yielding fifty four relevant papers which were considered in detail. While night-time specific studies identified very few papers (4 papers, 7%), most of the more general AT findings could be adopted to benefit night-time assistance. Studies have used technology for prompting and reminding as loss of time and forgetfulness are major problems; for monitoring daily activities in a sensor enriched environment and utilised location aware technologies to provide information to enhance safety. Technology also supports a range of therapies to alleviate symptoms. Therapies include the delivery of music and familial pictures for reminiscing, the use of light therapy to enhance wellbeing and the provision of mental tasks to stimulate the brain and maintain activity levels.

A new technique for identification of patients with congestive heart failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The identification system consists of two parts: feature extraction part and classification part. The feature extraction part uses the method of approximate spectral density estimation of R-R-Intervals (RRI) data by implementing the soft decision sub-band decomposition technique. In the classification part, two different methods of machine learning approaches with neural networks are implemented and compared in their performances. Those approaches are: supervised neural network (back-propagation) and unsupervised neural network (Kohonen self organizing maps).

The data used in this work is obtained from Massachusetts Institute of Technology (MIT) databases. A data set of 17 CHF and 53 normal subjects is used as original training data set, while another set of 12 CHF and 12 normal subjects is used as original test data set. The classification features are the spectral density of 6 different regions covering the whole spectrum of the RRI data obtained by 32-bands soft decision algorithm. A larger training data set, which is obtained by simulating 1000 CHF and 1000 normal subjects according to the spectral features obtained from the original training data, is used to train the neural network. The neural network is used then to test another simulated data set of the same size of the training date set (simulated according to the spectral features obtained from the original test data set). The accuracy of the classification is found to be about 83.65% and 91.43% with supervised neural networks and unsupervised neural networks respectively.

This paper presents a new method of trajectory planning in rehabilitation robotics. First were measured in healthy subject the pick to place trajectories while haptic robot is in zero impedance space. B-spline approximation is used to mathematically define the measured paths. This trajectory path serves as a central line for the rounding haptic tunnel. In addition to radial elastic and damping force an optional guidance force can be applied along the tunnel to reach the place point. The B-spline control points were observed around the robot and arm workspace. The trajectory path defined with B-splines is compared with minimum jerk and minimum torque defined trajectories. Finally are compared the pick to place movements with and without tunnel use in healthy subject and in stroke hemiplegic patient.

The main problems in intra-medullary nailing of femoral shaft-fractures are leg-length discrepancies and rotational differences with an incidence of 2–18% and 20–40% respectively. These may lead to severe postoperative sequelae such as additional correctional operations and difficult rehabilitation. Insufficient visualization can be considered the main reason for these complications. Finally, retention of the fragments in the correct alignment before nail insertion is difficult.

To overcome these problems we established a robotic telemanipulator system to support the reduction process. It was evaluated in 30 fractures of embalmed human femora. Specially programmed software used an image-dataset which was acquired by an isocentric 3D fluoroscope. For visualization, a surface projection was generated. Localization and tracking of the fragments and the robot-arm as well as accuracy measurement was performed by using an optical navigation system. Manipulation was controlled via a force-feedback joystick. This way, collisions of the fragments were transmitted back to the surgeon. At the end of the reduction the robot could rigidly retain the fragments' position.