[Clinical value of instrumental gait analysis]. / Stellenwert der instrumentellen Ganganalyse in der klinischen Versorgung.

BACKGROUND: Instrumental gait analysis is becoming an established addition to conventional diagnostic methods for the clinical assessment of complex movement disorders. It can provide objective and high resolution motion data and contains information that is not observable with conventional clinical methods, such as muscle activation during gait. UTILISATION: Instrumental gait analysis can add observer independent parameters to the treatment planning of individuals as well as provide insights into pathomechanisms with clinical research studies. Limiting factors for the use of gait analysis technology are currently the time and personnel expenditures for measurements and data processing, as well as the extensive amount of training time required for data interpretation. This article illustrates the clinical value of instrumental gait analysis and specifies its synergies with conventional diagnostic methods.

Do we still need to screen our patients?-Orthopaedic scoring based on motion tracking.

PURPOSE: Orthopaedic scores are essential for the clinical assessment of movement disorders but require an experienced clinician for the manual scoring. Wearable systems are taking root in the medical field and offer a possibility for the convenient collection of motion tracking data. The purpose of this work is to demonstrate the feasibility of automated orthopaedic scorings based on motion tracking data using the Harris Hip Score and the Knee Society Score as examples. METHODS: Seventy-eight patients received a clinical examination and an instrumental gait analysis after hip or knee arthroplasty. Seven hundred forty-four gait features were extracted from each patient's representative gait cycle. For each score, a hierarchical multiple regression analysis was conducted with a subsequent tenfold cross-validation. A data split of 70%/30% was applied for training/testing. RESULTS: Both scores can be reproduced with excellent coefficients of determination R2 for training, testing and cross-validation by applying regression models based on four to six features from instrumental gait analysis as well as the patient-reported parameter 'pain' as an offset factor. CONCLUSION: Computing established orthopaedic scores based on motion tracking data yields an automated evaluation of a joint function at the hip and knee which is suitable for direct clinical interpretation. In combination with novel technologies for wearable data collection, these computations can support healthcare staff with objective and telemedical applicable scorings for a large number of patients without the need for trained clinicians.

Evaluation of overall gait quality in Perthes disease. Are global gait indices of value for a local musculoskeletal disorder?

BACKGROUND: Until now follow-up- and outcome evaluations in Perthes disease are analysing predominantly subjective results and clinical/radiological parameters. To enlarge the assessment by quantitative, functional aspects a summary measure for overall gait quality would be helpful. Therefore, the objective of this study is to evaluate whether commonly used global gait indices are sensitive to detect gait deviations during the early stages of the Perthes disease. METHODS: 3D gait data of 70 patients scheduled for containment improving surgery with the diagnosis of Perthes were included. A group of 31 healthy children served as a control group. Based on 4 gait cycles of each subject the Gait Deviation Index, the Gillette Gait Index, the Hip Flexor Index and the Gait Deviation Index-Kinetic were calculated for the involved and non-involved side. The gait indices were compared (1) between patients and controls, (2) among patients sub-grouped by ROM/radiological classifications and (3) between the two limbs. FINDINGS: All applied gait indices besides the Gait Deviation Index-Kinetic of the patient-group are significantly different from the controls. The subgroup-analysis resulted only in significant differences between the radiological groups Herring B and C for the Gillette Gait Index. Comparing involved and non-involved side showed no significant differences. INTERPRETATION: The evaluated gait indices are sensitive to identify a pathologic gait pattern in Perthes disease, so they can be used as a functional outcome parameter evaluating treatment concepts. Nevertheless, these are not applicable to identify the pathologic side indicating that a local hip problem leads to global gait deviations.

A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke - The Stroke Mobility Score (SMS).

BACKGROUND For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). MATERIAL AND METHODS In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. RESULTS (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach's alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore "gait speed" are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. CONCLUSIONS The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.