Chedokee‐McMaster (CM) assessment, the Fugl‐Meyer (FM) assessment and the Wolf Motor Function Test (WMFT) assessment are for stroke. The Fahn‐Marsden rating scale (F‐M) [58], the Global Dystonia Rating Scale (GDRS) [12], the Unified Dystonia Rating Scale (UDRS) [11] and so on [22] were developed for dystonia. The Parkinson's Disease Questionnaire (PDQ‐39) [284] and its shorter version (PDQ‐8) [159], as well as the Parkinson's Disease Quality of Life (PDQL) questionnaire [144], the Webster [375] and the Unified Parkinsonâs Disease Rating Scale (UPDRS) [227] were developed for Parkinson's disease. More relevant to our work, Lane et al. [192] developed the Abnormal Involuntary Movement Scale (AIMS) to assess patients with dyskinesia. In this scale, the amplitude of involuntary movements was taken into consideration. In addition, Goetz et al. [119] proposed to use the Objective Dyskinesia Rating Scale (also known as the Rush Dyskinesia scale) to assess the severity of dyskinesia.
1.6.2 Automated kinematic performance assessment
Recently, with the development of sensing technologies, a number of approaches have been proposed to either automate conventional testing scales or develop new methods. Some examples are shown as follows.
Knorr et al. [177] automated two tasks, including reaching to the front and to the side, in the WMFT for people after stroke. Three accelerometers were attached to the hand, the corresponding forearm and the upper arm respectively to capture the characteristics of motion patterns. Eventually, two linear features (root mean square errors of acceleration and jerk) and a non‐linear feature (approximate entropy of acceleration) were evaluated as the measurements of functional limitation and motor impairment. Similarly, Wade et al. [369] tried to automate the widely used WMFT for post‐stroke patients. In their proposal, one IMU was attached to the wrist of the subject to measure the time used by the subject to finish each task in different WMFT tasks. Hester et al. [143] utilised 14 features to predict the clinical measurement scores of patients with stroke. These features were extracted from data collected by four accelerometers, three of which were attached to the affected hand, forearm and upper arm, while the fourth sensor was on the trunk. As for the tasks, different from the two previous studies, some tasks were not included in the WMFT. The process of predicting clinical scores from these 14 features is shown in Figure 1.12. Furthermore, Leibowitz et al. [202] introduced a protocol to quantitatively measure the proprioception deficit. In the protocol, the affected hand was located under a square board so that it could not be seen by the subject. After passively moving the impaired hand to one of four locations, the healthy hand was required to be moved to the same location actively. Eventually, the positions of both hands were measured by a MiniBIRD500 magnetic tracking system and the positional differences between the impaired and non‐impaired hands were computed as an indicator.
Figure 1.12 The process of predicting clinical scores from 14 features.
1.6.3 Summary and challenge
In the rehabilitation field, assessing the performance of limb functions is of importance in evaluating other medical procedures [219]. This task can be relatively easy to achieve in clinical environments. However, when it comes to telerehabilitation, it becomes difficult due to the absence of well‐trained clinicians in the majority of cases. From the literature, it is found that a number of features have been used to perform automated assessment. However, the majority of them are calculated from a dynamic aspect of movements, such as velocity, acceleration and jerk. Therefore, a challenge here is whether it is possible to derive features for kinematic movement assessment based on shape information from motion trajectories. A novel approach to evaluate patients' kinematic performance by investigating both the shape and dynamics of the motion trajectories will be discussed in Chapter 2.
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