Measuring the effects of seating on people with profound and multiple disabilities--a preliminary study.

This paper describes a preliminary study to investigate a range of approaches that might be used for measuring the effects of special seating on people with profound and multiple disabilities and their carers. A number of tools are proposed for measuring the effects on quality of life, function and carer satisfaction. The results of applying these tools to measure the effects of intervention with customized molded seating on nine people with multiple disabilities are described. The results suggest that these tools are sensitive to this intervention, showing a general beneficial effect with good carer satisfaction. The study points the way towards application of these tools to people with a wider range of disabilities and to different interventions.

Automatic detection of gait events: a case study using inductive learning techniques.

One of the problems which occurs in the development of a control system for functional electrical stimulation of the lower limbs is to detect accurately specific events within the gait cycle. We present a method for the classification of phases of the gait cycle using the artificial intelligence technique of inductive learning. Both the terminology of inductive learning and the algorithm used for the analyses are fully explained. Given a set of examples of sensor data from the gait events that are to be detected, the inductive learning algorithm is able to produce a decision tree (or set of rules) which classify the data using a minimum number of sensors. The nature of the redundancy of the sensor set is examined by progressively removing combinations of sensors and noting the effect on both the size of the decision trees produced and their classification accuracy on 'unseen' testing data. Since the algorithm is able to calculate which sensors are more important (informative), comparisons with the intuitive appreciation of sensor importance of five researchers in the fields were made, revealing that those sensors which appear intuitively most informative may, in fact, provide the least information. Comparison results with the standard statistical classification technique of linear discriminant analysis are also presented, showing the relative simplicity of the inductively derived rules together with their good classification accuracy. In addition to the control of FES, such techniques are also applicable to automatic gait analysis and the construction of expert systems for diagnosis of gait pathologies.