Gait analysis using gravitational acceleration measured by wearable sensors

Takeda, Ryo; Tadano, Shigeru; Todoh, Masahiro; Morikawa, Manabu; Nakayasu, Minoru; Yoshinari, Satoshi

doi:10.1016/j.jbiomech.2008.10.027

Cited by 115 publications

(54 citation statements)

References 21 publications

(20 reference statements)

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“…However, in dynamic states such as the gait, a translational acceleration component will become included. The authors (Takeda et al, 2009) have used the cyclic patterns in acceleration data during gait to create an algorithm to obtain optimal gravitational acceleration patterns. Here, an optimal three-dimensional representation for a person in the base coordinate system was reported, but there were differences in joint angles established with a camera based system.…”

Section: Introductionmentioning

confidence: 99%

Gait posture estimation using wearable acceleration and gyro sensors

Takeda

Tadano

Natorigawa

et al. 2009

Journal of Biomechanics

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159

124

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A method for gait analysis using wearable acceleration sensors and gyro sensors is proposed in this work. The volunteers wore sensor units that included a tri-axis acceleration sensor and three single axis gyro sensors. The angular velocity data measured by the gyro sensors were used to estimate the translational acceleration in the gait analysis. The translational acceleration was then subtracted from the acceleration sensor measurements to obtain the gravitational acceleration, giving the orientation of the lower limb segments. Segment orientation along with body measurements were used to obtain the positions of hip, knee, and ankle joints to create stick figure models of the volunteers. This method can measure the three dimensional positions of joint centers of the hip, knee, and ankle during movement. Experiments were carried out on the normal gait of three healthy volunteers. As a result, the flexion-extension (F-E) and the adduction-abduction (A-A) joint angles of the hips and the flexion-extension (F-E) joint angles of the knees were calculated and compared with a camera motion capture system. The correlation coefficients were above 0.88 for the hip F-E, higher than 0.721 for the hip A-A, better than 0.924 for the knee F-E. A moving stick figure model of each volunteer was created to visually confirm the walking posture. Further, the knee and ankle joint trajectories in the horizontal plane showed that the left and right legs were

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Section: Introductionmentioning

confidence: 99%

Gait posture estimation using wearable acceleration and gyro sensors

Takeda

Tadano

Natorigawa

et al. 2009

Journal of Biomechanics

Self Cite

159

124

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“…When the threshold of VAF was set to 0.8, only one synergy could be extracted from To explore the effect of crawling speed on the inter-limb coordination, limb phase lags at different speeds were calculated and analyzed in this study. In related studies, limb events like connection or separation were obtained by cameras [6,30], force sensors [31][32][33], or accelerometers [34][35][36]. In this study, ACC signals were used to detect the initiation of the stance phase of each limb and to calculate the ipsilateral phase lag, contralateral phase lag, and diagonal phase lag, since the contact or separation between the limbs and ground was accompanied by a sudden change of the acceleration of a certain limb.…”

Section: Inter-limb Coordination Analysismentioning

confidence: 99%

Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis

Chen

Niu

et al. 2017

Entropy

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Abstract:To investigate the intra-and inter-limb muscle coordination mechanism of human hands-and-knees crawling by means of muscle synergy analysis, surface electromyographic (sEMG) signals of 20 human adults were collected bilaterally from 32 limb related muscles during crawling with hands and knees at different speeds. The nonnegative matrix factorization (NMF) algorithm was exerted on each limb to extract muscle synergies. The results showed that intra-limb coordination was relatively stable during human hands-and-knees crawling. Two synergies, one relating to the stance phase and the other relating to the swing phase, could be extracted from each limb during a crawling cycle. Synergy structures during different speeds kept good consistency, but the recruitment levels, durations, and phases of muscle synergies were adjusted to adapt the change of crawling speed. Furthermore, the ipsilateral phase lag (IPL) value which was used to depict the inter-limb coordination changed with crawling speed for most subjects, and subjects using the no-limb-pairing mode at low speed tended to adopt the trot-like mode or pace-like mode at high speed. The research results could be well explained by the two-level central pattern generator (CPG) model consisting of a half-center rhythm generator (RG) and a pattern formation (PF) circuit. This study sheds light on the underlying control mechanism of human crawling.

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“…Medical care applications make use of biosensors and environmental sensors in order to obtain comprehensive health status information of the patients, including ECG, heart rate, blood pressure, skin temperature, and oxygen saturation [13]. Fall and movement detection applications are focused on the physiological conditions such as posture and fall detection for people that need special care like the elderly people who are susceptible to sudden falls which may lead to death, infants, or patients recovering from an operation [14,15].…”

Section: Healthcare Monitoring Applicationsmentioning

confidence: 99%

Wireless Healthcare Monitoring with RFID-Enhanced Video Sensor Networks

Alemdar

Durmus

Ersoy

2010

International Journal of Distributed Sensor Networks

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In pervasive healthcare systems, WSNs provide rich contextual information and alerting mechanisms against odd conditions with continuous monitoring. Furthermore, they minimize the need for caregivers and help the chronically ill and elderly to survive an independent life. In this paper, we propose an outdoor monitoring environment and evaluate the capabilities of video sensor networks for healthcare monitoring in an outdoor setting. The results exhibit that their capabilities are limited. For this reason, we proposed several enhancements for reducing the traffic load on the network for better performance. RFID is a very mature technology that has already been used in many areas. The RFID-enhanced video sensor networks reduce the network traffic load. Moreover, the proximity of the healthcare professionals who are also moving in the surveillance area is also used for better balancing the network load. Finally, for assuring the reporting of the emergency events with low latencies, we propose an emergency frame based queuing mechanism and evaluated its performance through simulations.

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Gait analysis using gravitational acceleration measured by wearable sensors

Cited by 115 publications

References 21 publications

Gait posture estimation using wearable acceleration and gyro sensors

Gait posture estimation using wearable acceleration and gyro sensors

Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis

Wireless Healthcare Monitoring with RFID-Enhanced Video Sensor Networks

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