Predicting lower limb joint kinematics using wearable motion sensors

Findlow, Andrew H.; Goulermas, John Y.; Nester, Christopher; Howard, David; Kenney, Laurence

doi:10.1016/j.gaitpost.2007.11.001

Cited by 148 publications

(111 citation statements)

References 16 publications

(21 reference statements)

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“…Recent advancements allow these sensors to be miniaturized, with faster processing capability and higher memory capacities to support outdoor applications [9]. The gyroscopes, in particular, are used for ambulatory gait assessment, foot-drop correction, control of lower limb prostheses and orthoses and other related clinical applications [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

Maqbool

Husman

Awad

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Lower extremity amputees suffer from mobility limitations which will result in a degradation of their quality of life. Wearable sensors are frequently used to assess spatiotemporal, kinematic and kinetic parameters providing the means to establish an interactive control of the amputee-prosthesisenvironment system. Gait events and the gait phase detection of an amputee's locomotion are vital for controlling lower limb prosthetic devices. The paper presents an approach to real-time gait event detection for lower limb amputees using a wireless gyroscope attached to the shank when performing level ground and ramp activities. The results were validated using both healthy and amputee subjects and showed that the time differences in identifying Initial Contact (IC) and Toe Off (TO) events were larger in a transfemoral amputee when compared to the control subjects and a transtibial amputee (TTA). Overall, the time difference latency lies within a range of ± 50 ms while the detection rate was 100% for all activities. Based on the validated results, the IC and TO events can be accurately detected using the proposed system in both control subjects and amputees when performing activities of daily living and can also be utilized in the clinical setup for rehabilitation and assessing the performance of lower limb prosthesis users.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

Maqbool

Husman

Awad

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Therefore, just attaching sensors such as the method of this study is a preferable preparation for the measurement. Considering the practical sensor attachment method, RMSE smaller than 5 deg and correlation coefficient larger than 0.95 were targeted in this study based on previous works [1], [3], [6], [7]. The Kalman filtering based measurement method stably reduced joint angle error and increased correlation coefficient almost achieving the target values, even if walking speeds or subjects were different.…”

Section: Discussionmentioning

confidence: 99%

“…In order to reduce the influence of the offset drift, an automatic resetting of the angle in each gait cycle and high-pass filtering were applied [1]. Joint angle estimation by the neural network was also reported [6]. Those methods, however, were not suitable for the rehabilitation because of large variety of gait of patients and needs of dc and low frequency information of angle data.…”

Section: Introductionmentioning

confidence: 99%

Kalman-Filtering-Based Joint Angle Measurement with Wireless Wearable Sensor System for Simplified Gait Analysis

Saito

Watanabe

2011

IEICE Trans. Inf. & Syst.

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SUMMARYThe aim of this study is to realize a simplified gait analysis system using wearable sensors. In this paper, a joint angle measurement method using Kalman filter to correct gyroscope signals from accelerometer signals was examined in measurement of hip, knee and ankle joint angles with a wireless wearable sensor system, in which the sensors were attached on the body without exact positioning. The lower limb joint angles of three healthy subjects were measured during gait with the developed sensor system and a 3D motion measurement system in order to evaluate the measurement accuracy. Then, 10 m walking measurement was performed under different walking speeds with a healthy subject in order to find the usefulness of the system as a simplified gait analysis system. The joint angles were measured with reasonable accuracy, and the system showed joint angle changes that were similar to those shown in a previous report as walking speed changed. It would be necessary to examine the influence of sensor attachment position and method for more stable measurement, and also to study other parameters for gait evaluation.

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“…More importantly, these sensors are cheap, non-invasive, portable, have low power and a fast microprocessor, as well as high memory capacity, allowing them to work for a longer period during indoor/outdoor ambulatory applications [13]. Gyroscopes in particular have been widely used for ambulatory gait analysis systems, for foot drop correction and for control of lower limb prostheses and orthoses and several other clinical applications [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Real-time gait event detection for transfemoral amputees during ramp ascending and descending

Maqbool

Husman

Awad

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Events and phases detection of the human gait are vital for controlling prosthesis, orthosis and functional electrical stimulation (FES) systems. Wearable sensors are inexpensive, portable and have fast processing capability. They are frequently used to assess spatio-temporal, kinematic and kinetic parameters of the human gait which in turn provide more details about the human voluntary control and amputeeprosthesis interaction. This paper presents a reliable real-time gait event detection algorithm based on simple heuristics approach, applicable to signals from tri-axial gyroscope for lower limb amputees during ramp ascending and descending. Experimental validation is done by comparing the results of gyroscope signal with footswitches. For healthy subjects, the mean difference between events detected by gyroscope and footswitches is 14 ms and 10.5 ms for initial contact (IC) whereas for toe off (TO) it is -5 ms and -25 ms for ramp up and down respectively. For transfemoral amputee, the error is slightly higher either due to the placement of footswitches underneath the foot or the lack of proper knee flexion and ankle plantarflexion/dorsiflexion during ramp up and down. Finally, repeatability tests showed promising results.

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Predicting lower limb joint kinematics using wearable motion sensors

Cited by 148 publications

References 16 publications

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

Kalman-Filtering-Based Joint Angle Measurement with Wireless Wearable Sensor System for Simplified Gait Analysis

Real-time gait event detection for transfemoral amputees during ramp ascending and descending

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