Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

Novak, Domen; Goršič, Maja; Podobnik, Janez; Munih, Marko

doi:10.3390/s141018800

Cited by 56 publications

(40 citation statements)

References 31 publications

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“…Cross-validation against direct observation resulted in a sensitivity and specificity of 94% and 89%, respectively. The sensitivity (99.4%) and specificity (99.4%) achieved by filtering gyroscope data at 0.25 Hz in the current study was higher than Novak et al (53) and Pham et al (55). Differences in sensitivity and specificity between studies for turn detection may be attributed to device location or filtering methods.…”

Section: Discussioncontrasting

confidence: 78%

“…An IMU is often used in navigation systems (63), though has received growing interest for the assessment of human movement such as gait analysis (65), postural orientation (45), and fall risk prevention (24) . The gyroscope and magnetometer components have also been used to detect turns and quantify turning characteristics (53,55,68), though there are limited studies that examine their application for estimating EE. walking, jogging, biking, and stair ascent and descent.…”

Section: Inertial Measurement Unitmentioning

confidence: 99%

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Application of the ActiGraph GT9X IMU for the assessment of turning during walking and running

Marcotte

2018

Biomed. Phys. Eng. Express

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Objective. The primary purpose of this study was to examine the use of the ActiGraph GT9X gyroscope and magnetometer for turn detection and quantifying turn degree during walking and running. The secondary purpose was to examine the effect of varying turn degrees during walking and running on oxygen consumption. Approach. Participants (N=17) completed pivot trials, straight-line treadmill walking and running (3-6 mph) and four turn conditions (45°, 90°, 135°, and 180°) during overground walking and running. Pivot trials were completed for 1 min and walking/running trials were completed for 6 min, both with a turn frequency of 10 turns/min. Data were collected using a GT9X device (right hip) and a Cosmed K4b 2 (measured oxygen consumption). Raw GT9X gyroscope and magnetometer data were processed through various low-pass filter frequencies (0.25-2.0 Hz). Treadmill and pivot trials were used to develop thresholds for turn detection using gyroscope and magnetometer data and cross-validated using the over-ground trials. Linear mixed models were used to compare actual and estimated number of turns, measured and estimated turn degree, and differences in VO 2 across walking and running speed and turn degree. Main results. Greater than 98% of turns were detected when using gyroscope data filtered at 0.25 Hz and turn degree was estimated within 2.2°of measured turn degree across all speeds. In general, the VO 2 of walking and running increased as the turn degree increased beyond 135°. Significance. The GT9X gyroscope, when filtered at 0.25 Hz can be used to detect the number of turns and estimate turn degree. The magnetometer, when filtered at 0.75 Hz, was useful for detecting the number of turns, however turn detection was more accurate using the gyroscope.

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Section: Discussioncontrasting

confidence: 78%

Section: Inertial Measurement Unitmentioning

confidence: 99%

Application of the ActiGraph GT9X IMU for the assessment of turning during walking and running

Marcotte

2018

Biomed. Phys. Eng. Express

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“…Based on the existing research results, we can get that the gait phase recognition generally used single-type sensors or a combination of multiple types of sensors [18], such as angular velocity, attitude, force, electromyography (EMG), IMU, camera, and so on [24,25,26,27,28,30,36,37,38]. However, for a lower-limb exoskeleton system, as few as possible sensors should be used to achieve the control goal, which can simplify the sensory system and enhance the stability of the exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Liu

Wang

et al. 2016

Sensors

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Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

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“…However, two problems remain: windowing (or motion gesture spotting) [31], and the null class [32]. Windowing is non-trivial, specifically the problem of identifying when a gesture began and when it ended in the data stream.…”

Section: Segmentation and Classificationmentioning

confidence: 99%

Wearable IMU for Shoulder Injury Prevention in Overhead Sports

Rawashdeh

Rafeldt

Uhl

2016

Sensors

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Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator.

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Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

Cited by 56 publications

References 31 publications

Application of the ActiGraph GT9X IMU for the assessment of turning during walking and running

Application of the ActiGraph GT9X IMU for the assessment of turning during walking and running

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

Wearable IMU for Shoulder Injury Prevention in Overhead Sports

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