Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults

Kong, Wei; Lin, Jer‐An; Waaning, L.; Sessa, Salvatore; Cosentino, Sarah; Magistro, Daniele; Zecca, Massimiliano; Kawashima, Ryuta; Takanishi, Atsuo

doi:10.1109/robio.2016.7866633

Cited by 14 publications

(13 citation statements)

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“…Although methods derived from low back-or footmounted IMUs [12,13,20,47] may be less susceptible to turns, literature suggested that GE detection is easier and more robust from shank-mounted IMUs. [17][18][19] Furthermore, to continue improving the current methods, and to be less dependent on the forward swinging motion of the leg, future research may also include vertical acceleration signals [13,37] or include information from both the time and frequency domain [34,48].…”

Section: Discussionmentioning

confidence: 99%

“…Identification of gait events (GEs) and phases is considered essential for clinical gait assessment [8]. GEs can be detected from a single low back-mounted IMU [11][12][13][14][15][16], however findings suggest that detecting GEs is easier from shank-or foot-mounted IMUs [17][18][19] where footmounted IMUs increase errors, especially in pathological gait patterns [19,20].…”

Section: Introductionmentioning

confidence: 99%

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

Romijnders

Warmerdam

Hansen

et al. 2021

Preprint

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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 ≥ 99%, F1 score =100%; FC: recall = 100%, precision ≥ 99%, F1 score =100%), and turning (IC: recall ≥ 85%, precision ≥ 95%, F1 score ≥ 91%; FC: recall ≥ 84%, precision ≥ 95%, F1 score ≥ 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Romijnders

Warmerdam

Hansen

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Some studies considered more realistic gait scenarios, and the gait phases were classified on different terrains [ 6 ], such as level-ground, ramp ascent/descent, and stair ascent/descent. Recently, researchers have focused their attention to gait events recognition during walking with cognitive tasks [ 7 , 8 ]. Referring to terrain recognition, the sEMG of the lower limbs during walking was acquired to identify five terrains [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

Zhong

Yang

et al. 2021

Sensors

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Gait phase detection on different terrains is an essential procedure for amputees with a lower limb assistive device to restore walking ability. In the present study, the intent recognition of gait events on three terrains based on sEMG was presented. The class separability and robustness of time, frequency, and time-frequency domain features of sEMG signals from five leg and back muscles were quantitatively evaluated by statistical analysis to select the best features set. Then, ensemble learning method that combines the outputs of multiple classifiers into a single fusion-produced output was implemented. The results obtained from data collected from four human participants revealed that the light gradient boosting machine (LightGBM) algorithm has an average accuracy of 93.1%, a macro-F1 score of 0.929, and a calculation time of prediction of 15 ms in discriminating 12 different gait phases on three terrains. This was better than traditional voting-based multiple classifier fusion methods. LightGBM is a perfect choice for gait phase detection on different terrains in daily life.

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“…In this paper, a kernel LDA-based nonlinear fusion model is proposed for gait phase recognition. To the best of our knowledge, traditional studies do not consider walking in the case of imprecise events, but in actual scenarios walking is usually accompanied by some cognitive tasks [28]. Therefore, two scenarios of walking, i) walking without cognitive tasks and ii) walking with action, are considered.…”

Section: Introductionmentioning

confidence: 99%

IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

Peng

Zhang

et al. 2019

IEEE Access

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Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait walking events when the subject is focusing only on walking, and the accuracy of traditional algorithms is susceptible to more realistic gait scenarios, such as walking with cognitive tasks. In this paper, the gait phase detection is considered in more realistic and challenging scenarios, in which the subject is walking while performing cognitive tasks. A kernel linear discriminant analysis (LDA)-based nonlinear fusion model is proposed for gait recognition, which can effectively reduce the error caused by cognitive tasks, making it an ideal model for gait phase detection while cognitive tasks are performed in the process of walking. Furthermore, the Internet of Things (IoT) framework is incorporated to reduce the gait phase detection algorithm's process time by offloading the data from local sEMG sensors to the IoT server with powerful computation capability. The experiments have been conducted to validate our proposed algorithms, demonstrating that the boundaries between the stance period and swing period are more blurred when walking with cognitive tasks.

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Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults

Cited by 14 publications

References 14 publications

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

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

Locomotion Mode Recognition for Walking on Three Terrains Based on sEMG of Lower Limb and Back Muscles

IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

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