Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests

Abstract

The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes may help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient’s age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II-III and concurrence of polyneuropathy.

Permutation Entropy and Irreversibility in Gait Kinematic Time Series from Patients with Mild Cognitive Decline and Early Alzheimer’s Dementia

“…IGA assesses patient’s specific problems by measuring how the body moves as a whole, by providing dozens of spatiotemporal parameters (e.g., walking speed or step length), and by further acquiring high-frequency kinematic measurements of those joints that align the lower extremity segments along the patient’s gait cycle [ 34 ]. Analysing those hundreds of parameters is not straightforward, and the solution may come from data mining techniques, which have enough power to classify and relate them to assess the effect of a condition [ 35 , 36 ].…”

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

“…Compared with computer vision-based, most ML-based gait analysis adopt IMU-based sensor systems [ 63 , 64 , 65 , 66 , 67 ]. The ML techniques used in IMU-based gait analysis include decision tree (DT) [ 68 ], linear discriminant analysis (LDA) [ 69 ], k-nearest neighbors (k-NN) [ 70 ], support vector machine (SVM) [ 67 ], CNN [ 71 , 72 ], random forest (RF) [ 61 , 66 , 73 ], and LSTM [ 49 ]. The efficiency of ML algorithms on Azure Kinect computer vision-based gait analysis is still not clear.…”

Pelvic Symmetry Is Influenced by Asymmetrical Tonic Neck Reflex during Young Children’s Gait

“…The kind of measurement that we proposed was conducted for the first time, so there is no strict comparison with other research. The most common gait analysis is conducted in children with scoliosis or cerebral palsy (CP) or in adults with hemiplegia [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. We have tried to compare some of the results, while keeping cautions.…”