Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients

Abstract

Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

“…If this and related conditions are not diagnosed promptly, the patients are more likely to suffer further injury by falling [1,2]. Recently, human activity recognition (HAR) was introduced to monitor the motion of a subject in daily life using healthcare devices to determine measures to prevent such accidents [3][4][5].…”

“…Because human health problems are most often expressed as measurable behaviors [11], IMUs are more suitable for daily activity data collection than other sensors. Hence, many IMUbased HAR studies have been accomplished [3][4][5]9]. Digo's study [12] was conducted to effectively recognize the working condition by wearing only one IMU in the trunk position.…”

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms

“…However, it is less clear how these curved trajectories impact the accuracy of various gait detection algorithms. One recent study investigated the impact of curved walking, turning, and cognitive load on detection performance of gait events using shank-mounted IMUs [19]. Results showed that detection performance was adequate for curved walking, but there were more false events detected during turning.…”

Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

“…An IMU utilizes three axial accelerometers, gyroscopes and geomagnetometers [ 7 ]. The validity of IMUs for gait event detection has been well established [ 9 , 10 ], while evidence is limited for movement phase detection during sports performance. IMUs have several advantages over conventional video analyses.…”