The Use of Accelerometers and Gyroscopes to Estimate Hip and Knee Angles on Gait Analysis

Alonge, F.; Cucco, Elisa; D’Ippolito, Filippo; Pulizzotto, A.

doi:10.3390/s140508430

Cited by 58 publications

(48 citation statements)

References 24 publications

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“…Low-cost wearable sensors offer an exciting alternative to traditional motion capture techniques [12,13]. In the present study, we present a unique implementation of an IMU in conjunction with Knee angles and tibial angular velocities strongly agreed (r xy ≥ 0.84) between the device (dashed blue) and motion capture (solid gold).…”

Section: Discussionmentioning

confidence: 63%

“…These subjects had a lower body mass index (23.8 ± 3.7) than many patient populations, including those undergoing total knee arthroplasty [11] and a single investigator placed the sensor on all research subjects. Soft-tissue artifact around the knee joint [4] likely affected our measurements and other technical paradigms may be less sensitive to skin motion [12,13].…”

Section: Discussionmentioning

confidence: 99%

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Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Hullfish

Stoeckl

et al. 2018

Preprint

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Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. Therefore, the purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Knee flexion was quantified using a 9-axis motion sensor and directly compared to motion capture data. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (RMS error: 2.6 degrees, P = 0.29) but less accurate during sit-to-stand exercises (RMS error: 16.6 degrees, P = 0.48). Knee flexion during gait strongly correlated (0.84 ≤ rxy ≤ 0.99) with motion capture measurements but demonstrated average errors of 10 degrees. This study demonstrated a low-cost sensor that satisfied our criteria: a simple calibration procedure resulting in accurate measures of joint function during clinical exercises, making it a feasible tool for continuous patient monitoring to guide regenerative rehabilitation.

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Section: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Hullfish

Stoeckl

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…To obtain knee angles from IMUs two devices have to be positioned on the shank and thigh of the subject to extrapolate the relative movement between the two segments. The output accuracy is affected by drift from required integration of acceleration and angular velocity values and, artefacts errors due to skin movement and misalignments [18], [19]. In addition to this, their use still requires a certain level of expertise that can limit wide adoption especially in the ageing population.…”

Section: Introductionmentioning

confidence: 97%

Smart Sensing System for Combined Activity Classification and Estimation of Knee Range of Motion

et al. 2015

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This paper proposes a novel wearable system and assesses its reliability in monitoring sagittal knee movement, and discriminating between activities of daily living. The system consists of a flexible conductive polymer unit, embedded into a pair of leggings at the level of the knee, interfaced with a customized sensing node for wireless data acquisition. Design constraints included the need for the system to be unobtrusive, low cost, low power, and simple to use. The wearable system was evaluated through a series of trials conducted on healthy participants, tested on two different occasions, while walking, running, and going up and down a set of stairs. The waveforms of the sensor output resemble typical knee kinematics curves. An intraclass correlation coefficient greater than 0.8 was obtained for the output signal of the sensor from which, knee movement is derived for each of the different activities. Time and frequency domain features of the signal were used to discriminate between activities. Results show good discriminative capacity of selected parameters to an accuracy of 93% when employing a random forest analytical approach. These results suggest that the system can be used accurately to monitor both knee movement and activity performed in unconstrained environments, and thus suggesting its potential use to support knee rehabilitation.Index Terms-Activity discrimination, flexible sensor, knee rehabilitation, median frequency, power spectral density, random forest, range of motion.

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“…The data is usually transferred to a smartphone which allows keeping a training record for the athlete. In contrast to these smart bands, accelerometers have been used by many researchers to find gait phases, lean angles, accelerations, and velocities of runners [1,2,4,5]. There is no validation study, to our knowledge, which compared steps derived from smart bands with other measurement systems like IMU's and motion tracking systems.…”

Section: Introductionmentioning

confidence: 98%

Feature Extraction from MEMS Accelerometer and Motion Tracking Measurements in Comparison with Smart Bands during Running

Stamm

Hartanto

2018

The 12th Conference of the International Sports Engineering Association

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Athlete monitoring is a major field of interest for professional and recreational runners as well as for coaches to improve performance and reduce injury risk. The development of inertial sensors in recent years offers the opportunity to improve the number of monitored training sessions significantly. This research used a self-developed inertial sensor in conjunction with a motion tracking system and four smart bands to record the runner's movement and extract parameters such as step numbers and frequencies. The data recorded were calibrated before it was high-pass filtered to remove gravity components from the signal. A peak detection algorithm was developed to find the number of steps, which have been further used to compare the different systems (IMU, motion capture, smart bands) and find their agreement. The results showed a very strong correlation between the IMU and the motion tracking system of r 2 = 0.998, and an r 2 = 0.996 between the IMU and one smart band.

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The Use of Accelerometers and Gyroscopes to Estimate Hip and Knee Angles on Gait Analysis

Cited by 58 publications

References 24 publications

Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Smart Sensing System for Combined Activity Classification and Estimation of Knee Range of Motion

Feature Extraction from MEMS Accelerometer and Motion Tracking Measurements in Comparison with Smart Bands during Running

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