Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis

Aliénor Vienne-Jumeau; Laurent Oudre; Albane Moreau; Flavien Quijoux; Sébastien Edmond; Mélanie Dandrieux; Eva Legendre; Pierre Paul Vidal; Damien Ricard

doi:10.3389/fneur.2020.00261

Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis

Front Neurol. 2020 Apr 21:11:261. doi: 10.3389/fneur.2020.00261. eCollection 2020.

Authors

Aliénor Vienne-Jumeau¹, Laurent Oudre^{1

2

3}, Albane Moreau⁴, Flavien Quijoux^{1

5}, Sébastien Edmond⁴, Mélanie Dandrieux⁴, Eva Legendre⁴, Pierre Paul Vidal^{1

6}, Damien Ricard^{1

4

7}

Affiliations

¹ COGNAC-G (UMR 8257), CNRS Service de Santé des Armées, University Paris Descartes, Paris, France.
² L2TI, University Paris 13, Villetaneuse, France.
³ CMLA (UMR 8536), CNRS ENS Paris-Saclay, Cachan, France.
⁴ Service de Neurologie, Hôpital d'Instruction des Armées Percy, Service de Santé des Armées, Clamart, France.
⁵ ORPEA Group, Puteaux, France.
⁶ Hangzhou Dianzi University, Zhejiang, China.
⁷ École du Val-de-Grâce, Ecole de Santé des Armées, Paris, France.

Abstract

Background: Objective gait assessment is key for the follow-up of patients with progressive multiple sclerosis (pMS). Inertial measurement units (IMUs) provide reliable and yet easy quantitative gait assessment in routine clinical settings. However, to the best of our knowledge, no automated step-detection algorithm performs well in detecting severely altered pMS gait. Method: This article elaborates on a step-detection method based on personalized templates tested against a gold standard. Twenty-two individuals with pMS and 10 young healthy subjects (HSs) were instructed to walk on an electronic walkway wearing synchronized IMUs. Templates were derived from the IMU signals by using Initial and Final Contact times given by the walkway. These were used to detect steps from other gait trials of the same individual (intra-individual template-based detection, IITD) or another participant from the same group (pMS or HS) (intra-group template-based detection, IGTD). All participants were seen twice with a 6-month interval, with two measurements performed at each visit. Performance and accuracy metrics were computed, along with a similarity index (SId), which was computed as the mean distance between detected steps and their respective closest template. Results: For HS participants, both the IITD and the IGTD algorithms had precision and recall of 1.00 for detecting steps. For pMS participants, precision and recall ranged from 0.94 to 1.00 for IITD and 0.85 to 0.95 for IGTD depending on the level of disability. The SId was correlated with performance and the accuracy of the result. An SId threshold of 0.957 (IITD) and 0.963 (IGTD) could rule out decreased performance (F-measure ≤ 0.95), with negative predictive values of 0.99 and 0.96 with the IITD and IGTD algorithms. Also, the SId computed with the IITD and IGTD algorithms could distinguish individuals showing changes at 6-month follow-up. Conclusion: This personalized step-detection method has high performance for detecting steps in pMS individuals with severely altered gait. The algorithm can be self-evaluating with the SI, which gives a measure of the confidence the clinician can have in the detection. What is more, the SId can be used as a biomarker of change in disease severity occurring between the two measurement times.

Keywords: accelerometer; gait detection; gait disorders; gait quantification; inertial measurement unit; multiple sclerosis; wearable inertial sensors.