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Background Cerebellar ataxia refers to the disturbance in movement resulting from cerebellar dysfunction. It manifests as inaccurate movements with delayed onset and overshoot, especially when movements are repetitive or rhythmic. Identification of ataxia is integral to the diagnosis and assessment of severity, and is important in monitoring progression and improvement. Ataxia is identified and assessed by clinicians observing subjects perform standardised movement tasks that emphasise ataxic movements. Our aim in this paper was to use data recorded from motion sensors worn while subjects performed these tasks, in order to make an objective assessment of ataxia that accurately modelled the clinical assessment. Methods Inertial measurement units and a Kinect© system were used to record motion data while control and ataxic subjects performed four instrumented version of upper extremities tests, i.e. finger chase test (FCT), finger tapping test (FTT), finger to nose test (FNT) and dysdiadochokinesia test (DDKT). Kinematic features were extracted from this data and correlated with clinical ratings of severity of ataxia using the Scale for the Assessment and Rating of Ataxia (SARA). These features were refined using Feed Backward feature Elimination (the best performing method of four). Using several different learning models, including Linear Discrimination, Quadratic Discrimination Analysis, Support Vector Machine and K-Nearest Neighbour these extracted features were used to accurately discriminate between ataxics and control subjects. Leave-One-Out cross validation estimated the generalised performance of the diagnostic model as well as the severity predicting regression model. Results The selected model accurately ($$96.4\%$$ 96.4 % ) predicted the clinical scores for ataxia and correlated well with clinical scores of the severity of ataxia ($$rho = 0.8$$ r h o = 0.8 , $$p < 0.001$$ p < 0.001 ). The severity estimation was also considered in a 4-level scale to provide a rating that is familiar to the current clinically-used rating of upper limb impairments. The combination of FCT and FTT performed as well as all four test combined in predicting the presence and severity of ataxia. Conclusion Individual bedside tests can be emulated using features derived from sensors worn while bedside tests of cerebellar ataxia were being performed. Each test emphasises different aspects of stability, timing, accuracy and rhythmicity of movements. Using the current models it is possible to model the clinician in identifying ataxia and assessing severity but also to identify those test which provide the optimum set of data. Trial registration Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).

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Background Cerebellar ataxia (CA) is a complex motor disorder that exhibits various symptoms such as lack of movement accuracy, delayed motion and ataxic movements associated with gait, extremity and eye. Accurate assessment of ataxic movements forms an integral part, not only in the process of diagnosis, but also to monitor the severity of the neurodegenerative progression, particularly in a rehabilitation context. However, the current assessment schemes are mostly based on the subjective observation of experienced clinicians. Capturing the movement during standard upper limb tests using readily available motion sensors, this paper is intended to amalgamate the sensory information to obtain a more accurate and objective form of assessment. Methods An assessment scheme involving an inertial measurement system and a Kinect system was considered to quantify the degree of ataxia in four instrumented version of upper extremities tests, i.e. Finger Chase (FCT), Finger Tapping (FTT), Finger to Nose (FNT) and Dysdiadochokinesia (DDKT). Kinematic features from these tests were extracted to quantitatively define ataxic signs such as dysmetria, delay in timing, irregularity and instability. Using Feed backward feature elimination (FBE) and Quadratic discrimination analysis (QDA) and Ridge regression (RR), the features were selectively combined to improve the diagnosis and verify the association with clinical assessments by means of Leave-One-Out cross validation. Clinical ratings of the disease status were recorded using the Scale for the Assessment and Rating of Ataxia (SARA). Results We report statistical significance in identifying ataxia from movement features of the four tests. The combined information from the features provided a high accuracy in diagnosing CA subjects (96.4%) in addition to a promising result in predicting the severity of ataxia due to CA (rho=0.8, p<0.001). The severity estimation was also considered in a 4-level scale to provide a rating that is familiar to the current clinically-used rating of upper limb impairments. The combination of FCT and FTT achieve the most acceptable outcome among the considered subsets of the 4 tests. Conclusion The analysis of ataxia can be decomposed primarily into four affected dimensions, i.e. stability, timing, accuracy and rhythmicity. In the context of upper limb tests, the results of accurate classification and prediction of severity attributed mostly to the timing. Furthermore, the underlying approach uncovers the appropriate combination with a reduced number of tests for the assessment of CA utilising the clinical resources more effectively. Trial registration Human Research and Ethics Committee, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia (HREC Reference Number: 11/994H/16).