Érica Fontana Paiva
Érica Fontana Paiva
Malte Ollenschläger (M.Sc.), Nils Roth (M.Sc.), Dr. med. Martin Regensburger (Molekulare Neurologie Uniklinikum Erlangen), PD Dr. phil. Heiko Gaßner (Molekulare Neurologie Uniklinikum Erlangen), Dr.-Ing. Felix Kluge, Prof. Dr. Björn Eskofier
11 / 2021 – 05 / 2022
Hereditary Spastic Paraplegia (HSP) is a neurodegenerative disease which affects neurons in the spinal cord. This disorder weakens lower limbs leading to a progressive gait disorder . The disease severity can be assessed by the Spastic Paraplegia Rating Scale (SPRS), a 13-item scale designed to rate functional impairment of spastic paraplegia. It is rated by clinicians without any assistive devices and is thus rater-dependent and subjective .
Therefore, instrumented gait analysis systems have been developed, which can help to differentiate HSP from other diseases, for instance, spastic diplegia  and cerebral palsy . For data acquisition, standardized walking tests are performed and recorded using a sensor system. In order to analyse the acquired data, it is segmented into single strides and spatio-temporal gait parameters, and then stride time or length are calculated.
Methods for stride segmentation include peak detection , template-based , wavelet-based fractional analysis , dynamic time warping (DTW) , hidden Markov models (HMM) [5, 7] and local cyclicity estimation [6, 8]. For several cohorts of different neurodegenerative diseases it was found that HMMs are the preferable choice for stride segmentation [5, 6]. However, the HMMs differ in the stride definition they employ. While Martindale et al.  used the foot’s initial and terminal contact to predict stance and swing phases, Roth et al. made use of the stride segmentation suggested by Barth et al. , which detects the beginning and the end of a complete stride instead of the stance and swing phase separately.
On one hand, Martindale’s predictions can be used to directly assess temporal gait parameters, whereas Roth’s approach would need to be extended by a gait event detection. On the other hand, Martindale’s approach requires personalized models, whereas Roth’s HMM uses a general model. A further difference in these approaches is that Martindale et al. used data of HSP patients recorded at the lab while Roth et al. used free-living gait data of patients with Parkinson’s disease.
To date, it is unclear to what extent these two approaches differ in performance in a cohort of HSP patients. Therefore, these approaches will be compared in this thesis. Additionally to the HSP at-lab dataset, the student will make use of a home-monitoring dataset acquired by the department of molecular neurology, UK Erlangen. It contains data of five HSP patients, who performed standardized walking test three times a day for 14 days.
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