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From the literature, it is found that encoders used in human motion recognition are similar to those in physical telerehabilitation in many studies. For instance, features like trajectories, velocity, acceleration, angle, angular velocity and angular acceleration are most commonly used in both fields. Though patients with movement disorders usually have a limited range of motions, they may be required to do certain tasks so as to evaluate their ability to perform ADLs, which usually are composed of a series of simple movements.

Figure 1.11 Marker‐based hand tacking system. Source: Cordella et al. [78].

As a result, there remain challenges in developing formal descriptions and robust computational procedures for the automatic interpretation and representation of motions of patients. The majority of studies [92, 158] employed a variety of human motion encoders to recognise or decompose general movement, such as reaching, waving hands, jumping, walking and so on. Few of them investigated details in each general movement, for example, the even smaller atomic components included in these general movements that are of importance for syntactic and structural descriptions of human movements in detail, especially in a clinic and rehabilitation environment, where the details of movements of body parts require a form of motion language or, at least, syntax. A novel approach to encode human motion trajectories will be discussed in Chapter 3.