Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data Martin Ullrich 1 , Arne K¨ uderle 1 , Luca Reggi 2 , Andrea Cereatti 3 , Bjoern M. Eskofier 1 , and Felix Kluge 1 Abstract— Digital gait measures derived from wearable in- ertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson’s disease, and ten Huntington’s disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson’s sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures. I. I NTRODUCTION The objective analysis of gait using wearable sensor sys- tems is gaining importance in the treatment of patients with motor impairments [1]. Various studies have identified gait parameters captured by wearable inertial measurement units (IMUs) as digital measures for gait performance in different diseases like Parkinson’s disease (PD), Huntington’s disease (HD), and post-stroke Hemiparesis (HE) [2]–[4]. From a technical perspective, an important aspect in the signal processing pipeline for IMU-based gait analysis is the segmentation of the recorded data into meaningful portions. One basic unit of gait measurements is a stride, which describes the period from the initial foot contact (IC) of one foot until the next IC of the same foot in a gait cycle [5]. Stride-level information is for example essential for defining walking bouts [6], [7], the measurement of gait variability [8], or the computation of gait asymmetry [9], where differ- ences between left and right strides are investigated. In their review about the use of wearable motion sensors for gait assessment, Brognara et al. reported that a majority of studies prefers the use of only one single sensor unit, 1 Machine Learning and Data Analytics Lab, Friedrich-Alexander- Universit¨ at Erlangen-Nurnberg (FAU), Erlangen, Germany (e-mail: martin.ullrich@fau.de, arne.kuederle@fau.de, bjoern.eskofier@fau.de, felix.kluge@fau.de). 2 Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy (e-mail: andrea.cereatti@polito.it). 3 Health Sciences and Technologies (CIRI-SDV), University of Bologna, Bologna, Italy (e-mail: luca.reggi2@unibo.it). which is most often attached close to the center of mass of the participant (e.g. on the lower back) [10]. For this sensor setup, the detection of ICs is very well investigated [11]–[14]. Still, to convert a sequence of ICs into strides according to the definition given above, ICs of the same foot need to be grouped and hence the laterality of each IC must me determined. However, this information is usually not directly available from IC detection algorithms and, therefore, stride segmentation is not trivial. Breaks, turns or missed ICs in the detection contradict the assumption of a steady alternating sequence of left and right ICs. Thus, only the explicit determination of the laterality of the ICs allows to define stride borders and additionally enable to differentiate between left and right strides which is, for example, crucial for the investigation of gait asymmetry. To the best of our knowledge, there is only one publication describing an approach for the distinction of left and right ICs detected from a lower back-mounted sensor: McCamley et al. used the sign (positive or negative) of the lowpass filtered gyroscope signal of the vertical axis at the sample of the detected IC to determine the foot it was performed with [11]. However, the laterality assignment was not sep- arately evaluated in their article. Furthermore, only young and healthy participants took part in their study and the transferability to patients with movement disorders and thus potentially higher gait variability needs to be investigated. Considering the high relevance of stride level parameters, the left-right distinction is a crucial part of a lower back sensor-based gait analysis pipeline and needs further investi- gations, given the lack of suitable well evaluated algorithms. Therefore, the goal of this study is twofold: First, we propose potential improvements for the algorithm by McCamley et al. and evaluate the algorithm on data from patients with movement disorders. Second, we present a new approach for the left-right distinction based on supervised machine learning (ML) including a cross-validation. For the experi- ments, a data set including 40 participants with and without movement impairments, that was previously recorded and presented by Trojaniello et al. [15], was analyzed. The results of our study contribute to a better understanding and an increased reliability of stride-based gait analysis in single- sensor settings. II. METHODS A. Data set The data set contained four groups (healthy controls (HC), patients with Parkinsons disease (PD), patients with Huntington’s disease (HD), and hemiparetic patients (HE)) 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Oct 31 - Nov 4, 2021. 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