Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

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Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach

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Open AccessArticle

by

Chenhui Huang

^*,

Kenichiro Fukushi

,

Zhenwei Wang

,

Fumiyuki Nihey

,

Hiroshi Kajitani

and

Kentaro Nakahara

Biometrics Research Labs, NEC Corporation, Abiko 1131, Japan

^*

Author to whom correspondence should be addressed.

Academic Editor: Wan-Young Chung

Sensors 2022, 22(1), 351; https://doi.org/10.3390/s22010351

Received: 1 December 2021 / Revised: 22 December 2021 / Accepted: 1 January 2022 / Published: 4 January 2022

(This article belongs to the Special Issue Recent Advance and Application of Wearable Inertial Sensors in Motion Analysis)

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Abstract

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.

Keywords: gait parameters assessment; gait event detection; foot motion; gait analysis; in-shoe sensor; inertial measurement unit

1. Introduction

As a result of the rapid development of wearable device technologies, wearable smart motion sensors have been used in various healthcare applications based on daily gait analysis, with all data processing performed automatically on an edge device [1]. Recently, a new type of wearable smart motion sensor for daily gait analysis, called an “in-shoe motion sensor” (IMS), was developed. The IMS can be placed in various kinds of shoes or insoles, and is less inconvenient to wear [2,3]. Thus, it shows promise for various healthcare and activity monitoring applications, with the aim of improving the habits of healthy subjects involving daily walking, by assessing temporal gait parameters or detecting gait signal features through artificial intelligent technologies [4].

Common temporal gait parameters such as walking velocity, stride length, foot angle, gait variability, cadence, and foot clearance can currently be obtained in real time by automatic calculation in the microprocessor of an IMS [4,5]. In contrast, temporal gait parameters concerning bilateral lower limbs (GPBLLs) play more important roles in healthcare or daily activity monitoring applications, such as evaluation of walking ability [4], metabolic evaluation [6], daily fatigue monitoring [7], and alcohol use monitoring [8]. These parameters include the double support time (DST), which is defined as the duration when both bilateral lower limbs touch the ground in one gait cycle (GC), and the symmetry index of stride time (SIS_tr) and symmetry index of stance phase time (SIS_ta) between the two limbs. These GPBLLs are important because they capture the interaction of the two feet and are essential predictors for estimating certain deep gait parameters, such as gait asymmetry, lower limb muscle strength, and muscle strength transition ability from one limb to the other [5,6,9]. Furthermore, these deep gait parameters are significant predictors for metabolic monitoring, fatigue assessment, walking ability, body functions, and alcohol monitoring [5,6,7,9,10,11,12]. To expand the potential use of IMSs in healthcare or daily activity monitoring applications for healthy subjects, we believe it is necessary to conduct automatic measurement of these GPBLLs by an IMS.

To obtain the temporal GPBLLs, which are illustrated in Figure 1, it is necessary for foot motion signals to interact well with each other. The motions of both feet are commonly detected by placing two independent sensors on them. Measurement then requires the phase difference between the motions in the same GC to be correctly traced on the same temporal axis. A common approach is to temporally synchronize the sensors using external devices, i.e., via a hardware connection [13,14]. In a smart system, the IMSs are synchronized with each other by timestamps that originate from a universal time controller in the system, e.g., a smartphone. This may make the system more complicated and subject to the uncertainty in the sensor network, clock drift, and packet delays in the communication protocol [15,16].

Figure 1. Illustration of the definitions of a GC, gait phases, gait events, and certain temporal gait parameters, and information on the foot acceleration in the anterior–posterior direction, the knee joint angle, and the ankle joint angle in one GC.

Accordingly, in this study, by combining biomechanical knowledge with signal processing, we propose a new approach to overcome these issues and provide a simpler method with higher accuracy and precision for evaluation of temporal GPBLLs using only a single sensor. Walking is a natural form of periodic movement. Moreover, during walking, the motions of the two lower limbs intrinsically interact well with each other, according to a musculoskeletal model in which the phase difference between the two limbs is locked by the connection of the pelvis. Thus, once the motion of one foot is determined, the motion of the opposite foot can also be known almost immediately [17].

A GC is the time period or sequence of events or movements during locomotion from when a foot contacts the ground, which is called a “heel strike” (HS), to when that same foot again contacts the ground. The HS is one of seven defined gait events, i.e., special motions during walking. A GC can be partitioned into stance and swing phases, which constitute 60% and 40% of the GC, respectively, as shown in Figure 1. Furthermore, a GC can be divided into seven periods—loading response (LR), mid-stance (MSt), terminal stance (TSt), pre-swing (PS), initial swing (ISw), mid-swing (MSw), and terminal swing (TSw)—by the six gait events in addition to HS: opposite toe off (OTO), heel rise (HR), opposite heel strike (OHS), toe off (TO), feet adjacent (FA), and tibia vertical (TV) [17]. Specifically, taking the right foot as an example, the OHS and OTO events represent the respective moments when the left heel strikes the ground and the left toe departs from the ground, which suggests that the left foot’s exact temporal information for GPBLL estimation can be obtained by detecting these two gait events from the right foot. Hence, as long as the HS, TO, OHS, and OTO events of the right foot are appropriately detected from foot motion signals, temporal GPBLLs such as DST, SIS_tr, and SIS_ta can be captured well by a single IMS mounted on the right foot. In this case, hardware connection of two sensors would no longer be necessary to assess the interaction of two feet. We refer to this idea as the “gait event detection approach”, which requires an IMS to have a real-time gait event detection method.

In our previous study, we developed a rule-based real-time HS and TO detection method [18]. As shown in Figure 1, we discovered that the foot acceleration signal in the anterior–posterior direction exhibits a sharp valley next to the maximum, which can be used for HS detection, while the first valley in a W-shaped wave pattern appearing after a foot-flat state can be used for TO detection. The missing piece of this approach is a method of real-time OHS and OTO event detection for an IMS. Currently, many gait event detection methods for wearable sensor systems have been proposed [19,20]. Previously, González et al. [21] proposed a method using a foot pressure sensor, and Liu et al. [22] proposed a method based on fusion of data from multiple segments of the lower limbs to detect OHS and OTO. However, these methods cannot be used in an IMS system that only measures a single segment. For OHS and OTO detection using foot-mounted inertial measurement units (IMUs), Mariani et al. [23] developed hidden Markov models, and Kidziński et al. [24] proposed a deep learning method. Such methods that construct complicated models from training data require considerable processing power and, thus, they are difficult to implement in a microprocessor chip. In contrast, Rueterbories et al. [25] developed a simpler, rule-based OHS and OTO detection method, which was considered feasible for edge device processing. In their method, they determine gait events through detection of peaks and valleys from three types of low-pass-filtered composite signals, including a raw signal at 160 Hz and two moving-average raw signals with window lengths of 50 and 200; then, they analyze the relationships between detected feature points. However, their method was sensitive to individual walking velocity differences: when the deviation of the velocities from the average increased, the precision of estimation decreased. To achieve higher precision, improved OHS and OTO detection methods are thus needed.

According to modern kinesiology studies on walking, gait events correspond to the timings of muscle and joint activity patterns transiting from one state to another [17,26]. Following these studies, we hypothesize that gait events should appear as characteristic turning points, such as peaks, valleys, and zero-crossing points, in the foot motion signal acquired by an IMS. In the current study, we developed a rule-based method for OTO and OHS detection and applied it to GPBLL measurements. To do so, we first used biomechanical knowledge to analyze how the lower limbs move near the moments of OHS and OTO [17], and we then searched for possible feature points as candidates for OHS and OTO detection based on the aforementioned hypothesis. Next, we evaluated the accuracy and precision of our method through comparison with the measurement results obtained by a 3D motion analysis system. Given the similarity in motion between the left and right feet, if the feasibility of our method is demonstrated by an IMS mounted on the right foot, we assume that it should also be feasible for an IMS mounted on the left foot.

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