The overall objective of the project is to devise quantitative methods aimed at assessing the physical functional limitation of a given individual, to design the relevant equipment and produce a prototype in a precompetitive scenario. We will pursue this endeavour with reference to the locomotor system and in order to address the following specific clinical issues: prognosis, eligibility for health services, measure of the outcome of a therapy, therapeutic programming, fine tuning of orthosis and prosthesis alignment/fitting. By combining clinical and bioengineering knowledge, we will attempt to devise finalised solutions based on an ensemble of measuring instrument, experimental protocol, mathematical model, relevant interpretation procedures, that are applicable in a clinical setting and help in the clinical decision making process, and a software package with a user friendly interface. These solutions will integrate with and/or substitute for the functional evaluation tests currently adopted in clinical settings. In order to achieve the above-mentioned objectives, we will identify a number of motor acts the combined analysis of which may reveal the locomotor functional limitation profile of a given individual in all of its possible aspects. As a first choice, we will investigate the following motor tasks: orthostatic posture, body movements with fixed base of support (base of support habitability) such as rising from and sitting on a seat, ascent and descent of a step, squat, and then level walking and jumping. A thorough biomechanical analysis of these motor tasks utilizing present technology (e.g., stereophotogrammetry, dynamometry, electromyography) would be effective but awkward to apply for subject-specific evaluation in clinical practice by reason of the complexity of both instrumentation and experimental protocols. Therefore, new solutions that join objectivity with field applicability must be devised. To this purpose, a minimum number of biomechanical variables must be measured during the execution of the selected motor task and these quantities must be acquired using a low cost experimental apparatus least perceivable to the test subject. This project counts on the use of a wireless, wearable inertial sensor device (WISD, equipped with a graphic display) that provides body segment 3-D linear acceleration and orientation information. This device has already been developed and is being marketed for different purposes by a spin off enterprise (Loran Engineering srl). A WISD will be made available to all participants in the project in addition to the relevant assistance and maintenance. However, since data thus obtained do not necessarily lend themselves to straightforward interpretation in terms of function assessment, we will devise models of the musculo-skeletal system that embody the invariant aspects of both the modelled system and the specific motor task. Using such “minimum measured-input models”, we expect richer, physiology-related, and thus easier to interpret, information. These models will be implemented in the device firmware so that results will be available in real time on the WISD display. Data may also be transferred to a PC to be both fed to a database and subjected to advanced statistics analysis. It is important to emphasize that the performance of any new solution that would result from the present research work will be compared with that of standard clinical methods. This exercise will be carried out making reference to selected subject populations that are considered to be representative in terms of possible profiles of functional limitation in postural and locomotor behaviour and that have an important social impact: subjects affected by physiological senescence, Parkinson’s disease, muscular dystrophy, consequences of stroke, cerebral palsy, lower limb amputation and pathologies requiring the application of orthoses. It is evident that we will also acquire a database relative to able-bodied populations for comparison. Clinical and biomechanical data, acquired in the course of multi-centre trials, will allow designing a pattern recognition tool for clinical decision making, valuable to classify the single subjects under study.