Smart wearable device for real time gait event detection during running

Alahakone, A.U.; Senanayake, S. M. N. Arosha; Senanayake, C.M.

doi:10.1109/apccas.2010.5774975

Cited by 17 publications

(16 citation statements)

References 14 publications

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“…In opposition, the studies used non-portable motion capture systems to monitor the activity outcome measures. These motion capture systems only operate in controlled environments [46] and are not able to analyse consecutive gait cycles in daily locomotion activities [47,48]. They cannot be worn by the users, in opposition to the prescribed orthoses.…”

Section: Are the Motion Capture Systems Wearable Technology?mentioning

confidence: 99%

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Figueiredo

Moreno

Matias

et al. 2019

Disability and Rehabilitation: Assistive Technology

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To review and categorize, according to the International Classification of Functioning, the outcome measures, and motion capture systems for studying the evidence-based practice of orthotic-based interventions in post-stroke gait rehabilitation. Methods: An electronic literature search was conducted up to February 2018 in Web of Science, Scopus, MEDLINE, and Physiotherapy Evidence Database. Randomized trials measuring activity, impairment, or participation outcome measures for studying the evidence-based practice of orthoses in gait rehabilitation after an acute or chronic stroke were identified. The studies were assessed through the Cochrane risk-of-bias tool by three authors. Information about stroke's stage, assessment protocol (goal, timing, and motion capture system), orthosis configuration, and outcome measures were extracted. Results: Eighteen randomized trials, including 387 post-stroke adults, mostly in the chronic stage, were selected. They assessed 39 outcomes, mainly activity outcome measures such as spatiotemporal (72.2%), kinematic (44.4%), and functional (33.3%) outcomes. Gait speed was the primary outcome in most studies. Participation (22.2%) and impairment (16.7%) outcome measures were less explored. Mostly, non-portable motion capture systems were employed opposing the freely-use of the wearable orthosis. The detection bias risk and the shortage of baseline and follow-up outcome measures affected the studies' assessment quality.Conclusionsː Studies showed heterogeneity in selecting outcomes and timings for assessment. There is evidence for assessing the evidence of orthosis-based gait rehabilitation after stroke through activity outcome measures, primarily the gait speed, recorded by non-wearable motion capture systems. A unified methodology considering wearable sensors for tracking baseline and follow-up measures is needed.

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Section: Are the Motion Capture Systems Wearable Technology?mentioning

confidence: 99%

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Figueiredo

Moreno

Matias

et al. 2019

Disability and Rehabilitation: Assistive Technology

View full text Add to dashboard Cite

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“…motion systems. Nevertheless, these motion capture systems are non-portable [6], operate only in controlled environments [7], and are consequently not optimal to analyze consecutive gait cycles for long-term mobility scenarios [4].…”

Section: Introductionmentioning

confidence: 99%

Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures From Healthy Subjects

Figueiredo

Félix

Costa

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This study fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real-time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finitestate-machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from eleven healthy subjects (28.27±4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 km/h to 4.5 km/h) and slopes (0% to 10%). This validation also includes heterogenous gait patterns from nine healthy subjects (27±7.35 years) monitored at three self-selected paces (from 1±0.2 m/s to 2±0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate (p>0.9925) and time-effective (<30.53±9.88ms, p>0.9314) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel Strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy>96.98%). Misdetection was more pronounced in Middle Mid-Swing (accuracy>90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.

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“…[3]. Rule-based algorithm methods for defining gait classifiers have been implemented using a wearable three-axis inertial measurement unit (IMU) sensor attached to both shanks [13] or the thigh, shank, and foot [14]. Other discrete gait phase detection algorithms try to reduce classification errors by introducing more sensors, such as using force sensitive resistors (FSRs) under the shoe insole to measure ground contact forces [15]–[17].…”

Section: Introductionmentioning

confidence: 99%

Real-Time continuous gait phase and speed estimation from a single sensor

Quintero¹,

Lambert

Villarreal³

et al. 2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Human gait involves a repetitive cycle of movements, and the phase of gait represents the location in this cycle. Gait phase is measured across many areas of study (e.g., for analyzing gait and controlling powered lower-limb prosthetic and orthotic devices). Current gait phase detection methods measure discrete gait events (e.g., heel strike, flat foot, toe off, etc.) by placing multiple sensors on the subject’s lower-limbs. Using multiple sensors can create difficulty in experimental setup and real-time data processing. In addition, detecting only discrete events during the gait cycle limits the amount of information available during locomotion. In this paper we propose a real-time and continuous measurement of gait phase parameterized by a mechanical variable (i.e., phase variable) from a single sensor measuring the human thigh motion. Human subject experiments demonstrate the ability of the phase variable to accurately parameterize gait progression for different walking/running speeds (1 to 9 miles/hour). Our results show that this real-time method can also estimate gait speed from the same sensor.

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Smart wearable device for real time gait event detection during running

Cited by 17 publications

References 14 publications

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Outcome measures and motion capture systems for assessing lower limb orthosis-based interventions after stroke: a systematic review

Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures From Healthy Subjects

Real-Time continuous gait phase and speed estimation from a single sensor

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