Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy

Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.Comment: 7 pages, 4 figures; supplemental material 9 pages, 8 figures; to be published in the proceedings of the 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX

Evaluation von Stumpf-Schaft Verschiebungen mittels Ultraschall – Pilotversuch zur Reproduzierbarkeit der Methodik

<p>Verschiebungen zwischen Stumpf und Schaft können bei myoelektrischen Handprothesen mit Mustererkennung zu Fehlansteuerungen oder gar Ausfällen führen. Verantwortlich dafür sind Elektrodenverschiebungen, welche Fehlklassifizierungen der Muster verursachen können. Nicht nur das Aus- und Anziehen des Schaftes, sondern auch Armpositionen und Zusatzgewichte können das Risiko von Elektrodenverschiebungen erhöhen. Dies kann zu einer nicht adäquaten Ansteuerung führen und die Prothese kann im Alltag nicht mehr zweckentsprechend eingesetzt werden. Zur Quantifizierung der Verschiebungen, soll eine Methode entwickelt werden, die die Verschiebung zwischen Stumpf und Schaft möglichst präzise misst. Mittels Ultraschall- und 3D Bewegungsanalysesystem wird ein knöcherner Punkt im Ultraschallbild detektiert. Mit den vorliegenden vorläufigen Ergebnissen soll die Wiederholbarkeit der Methodik überprüft werden. Künftig soll dies zur Messung der Stumpf-Schaft Verschiebung bei Prothesenträgern eingesetzt werden.</p

Oberkörperanalyse im Bewegungslabor

<p>Presentation of upper limb motion analysis in the gait and motion laboratory of the orthopaedic Hospital Speising-Vienna (OSS).</p

Users' and therapists' perceptions of myoelectric multi-function upper limb prostheses with conventional and pattern recognition control

OBJECTIVE: To describe users' and therapists' opinions on multi-function myoelectric upper limb prostheses with conventional control and pattern recognition control. DESIGN: Qualitative interview study. SETTINGS: Two rehabilitation institutions in the Netherlands and one in Austria. SUBJECTS: The study cohort consisted of 15 prosthesis users (13 males, mean age: 43.7 years, average experience with multi-function prosthesis: 3.15 years) and seven therapists (one male, mean age: 44.1 years, average experience with multi-function prostheses: 6.6 years). Four of these users and one therapist had experience with pattern recognition control. METHOD: This study consisted of semi-structured interviews. The participants were interviewed at their rehabilitation centres or at home by telephone. The thematic framework approach was used for analysis. RESULTS: The themes emerging from prosthesis users and therapists were largely congruent and resulted in one thematic framework with three main themes: control, prosthesis, and activities. The participants mostly addressed (dis-) satisfaction with the control type and the prosthesis itself and described the way they used their prostheses in daily tasks. CONCLUSION: Prosthesis users and therapists described multi-function upper limb prostheses as more functional devices than conventional one-degree-of-freedom prostheses. Nonetheless, the prostheses were seldom used to actively grasp and manipulate objects. Moreover, the participants clearly expressed their dissatisfaction with the mechanical robustness of the devices and with the process of switching prosthesis function under conventional control. Pattern recognition was appreciated as an intuitive control that facilitated fast switching between prosthesis functions, but was reported to be too unreliable for daily use and require extensive training