Non-reviewed Conference Papers and Abstracts

For social species, including primates, the recognition of dynamic body actions is crucial for survival. However, the detailed neural circuitry underlying this process is currently not well understood. In monkeys, body-selective patches in the visual temporal cortex may contribute to this processing. We propose a physiologically-inspired neural model of the visual recognition of body movements, which combines an existing image-computable model (`ShapeComp') that produces high-dimensional shape vectors of object silhouettes, with a neurodynamical model that encodes dynamic image sequences exploiting sequence-selective neural fields. The model successfully classifies videos of body silhouettes performing different actions. At the population level, the model reproduces characteristics of macaque single-unit responses from the rostral dorsal bank of the Superior Temporal Sulcus (Anterior Medial Upper Body (AMUB) patch). In the presence of time gaps in the stimulus videos, the predictions made by the model match the data from real neurons. The underlying neurodynamics can be analyzed by exploiting the framework of neural field dynamics.

BACKGROUND AND AIM: With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid motor biomarkers are highly warranted, which detect longitudinal changes in short, trial-like time-frames. In this observational study, we aim to unravel biomarkers of ataxic gait which are sensitive for longitudinal changes in real life by using wearable sensors. We hypothesize that, gait measures captured in patients' real life could be more sensitive to progression in short, trial-like time-frames compared to lab-based gait assessments and clinical rating scales. However, in real life walking, gait measures are substantially influenced by contextual and environmental factors, as it has been shown in healthy subjects as well as for different patient populations. Thus, we introduce a context-sensitive matching procedure of individual walking bouts to reveal disease-related rather than purely context-driven longitudinal changes in variability measures. METHODS: We assessed longitudinal gait changes of 24 subjects with degenerative cerebellar disease (SARA:9.4±4.1) at baseline and 1-year and 2-year follow-up assessment by 3 body-worn inertial sensors in two conditions: (1) laboratory-based walking; (2) real-life walking during everyday living. In the real-life walking condition, a context-sensitive analysis was performed by selecting comparable walking bouts according to macroscopic gait characteristics namely bout length and number of turns within a two-minutes time interval. Movement analysis focussed on measures of spatio-temporal variability, in particular lateral step deviation (LD) and a compound measure of spatial variability (SPcmp). RESULTS: Cross-sectional analyses revealed high correlation to ataxia severity (SARA) and patients subjective balance confidence (ABC Scale) in both conditions (r > 0.8). While clinical ataxia score and gait measure in lab-based gait assessments identified changes after two years only (SARA: rprb = 0.71; LD: rprb = 0.67) in real life gait assessment the features of lateral step deviation and a compound measure of spatial step variability identified changes already prb after one year with high effect sizes (LD: rprb = 0.66; SPcmp: rprb = 0.68) and increased effect sizes after two years (LD: rprb = 0.77; SPcmp: rprb = 0.82). CONCLUSIONS: Utilizing a context-sensitive matching procedure, real-life gait measures capture longitudinal change within short trial-like time frames like 1 year with high effect size. In contrast, clinical scores like the SARA as well as lab-based gait measures show longitudinal change only after two years. Thus, features of real-life gait constitute promising biomarkers for upcoming therapeutical trials, delivering ecologically validity as well as increased effect sizes in comparison with clinical scores and lab-based gait assessment.

BACKGROUND AND AIM: The optimal number of inertial sensors for real-life gait analysis is a trade-off between data quality and patient convenience and feasibility. One-sensor systems have proven to deliver reliable information for average values of gait speed or step length. However, for the ataxic-sensitive measures of spatio-temporal gait variability, these systems reported less reliability and less sensitivity compared to 3 sensor systems including two sensors at the feet. Here, we investigate the potential of machine learning techniques to predict gait features based on 1 sensor only, which could increase the clinical feasibility of instrumented gait analysis in real-life recordings of cerebellar ataxic patients. METHODS: We recorded gait data from 44 healthy controls and 55 cerebellar patients at baseline, 1-year and 2-years follow-up assessments by 3 Opal APDM inertial sensors. These data successful identified longitudinal changes in gait variability measures for cerebellar patients (e.g. stride length variability, effect size: 0.53) Utilising 1D convolutional neural networks (1D-CNN) we predicted 14 gait parameters from stride based triaxial IMU data in two conditions with different input dimensions: using raw data from the pelvis sensor only (1S) in comparison to the complete set of all three sensors (3S). Thus, in the supervised training phase of both conditions, we used stride based gait features previously determined by the 3 sensors algorithm from APDM as ground truth. Aim in both approaches is to individualize the learned mappings for a new unseen patient based on a small amount of recorded gait samples with 3 sensors in the lab and to use transfer learning for the characterisation of real-life data. RESULTS: First results deliver a low (