Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter

Omar Salah; Ahmed A. Ramadan; Salvatore Sessa; Ahmed M R Fath El-Bab; Ahmed Abo-Ismail; M. Zecca; Yo Kobayashi; Atsuo Takanishi; M. Fujie

doi:10.1109/HIC.2014.7038931

Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter

Omar Salah, Ahmed A. Ramadan, Salvatore Sessa, Ahmed M R Fath El-Bab, Ahmed Abo-Ismail, M. Zecca, Yo Kobayashi, Atsuo Takanishi, M. Fujie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.

Original language	English
Title of host publication	2014 IEEE Healthcare Innovation Conference, HIC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	288-291
Number of pages	4
ISBN (Print)	9781467363648
DOIs	https://doi.org/10.1109/HIC.2014.7038931
Publication status	Published - 2015 Feb 10
Externally published	Yes
Event	2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States Duration: 2014 Oct 8 → 2014 Oct 10

Other

Other	2014 IEEE Healthcare Innovation Conference, HIC 2014
Country/Territory	United States
City	Seattle
Period	14/10/8 → 14/10/10

ASJC Scopus subject areas

Medicine(all)
Biomedical Engineering

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10.1109/HIC.2014.7038931

Cite this

Salah, O., Ramadan, A. A., Sessa, S., Fath El-Bab, A. M. R., Abo-Ismail, A., Zecca, M., Kobayashi, Y., Takanishi, A., & Fujie, M. (2015). Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter. In 2014 IEEE Healthcare Innovation Conference, HIC 2014 (pp. 288-291). Article 7038931 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HIC.2014.7038931

Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter. / Salah, Omar; Ramadan, Ahmed A.; Sessa, Salvatore et al.
2014 IEEE Healthcare Innovation Conference, HIC 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 288-291 7038931.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Salah, O, Ramadan, AA, Sessa, S, Fath El-Bab, AMR, Abo-Ismail, A, Zecca, M, Kobayashi, Y, Takanishi, A & Fujie, M 2015, Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter. in 2014 IEEE Healthcare Innovation Conference, HIC 2014., 7038931, Institute of Electrical and Electronics Engineers Inc., pp. 288-291, 2014 IEEE Healthcare Innovation Conference, HIC 2014, Seattle, United States, 14/10/8. https://doi.org/10.1109/HIC.2014.7038931

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title = "Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter",

abstract = "This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.",

author = "Omar Salah and Ramadan, {Ahmed A.} and Salvatore Sessa and {Fath El-Bab}, {Ahmed M R} and Ahmed Abo-Ismail and M. Zecca and Yo Kobayashi and Atsuo Takanishi and M. Fujie",

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AU - Salah, Omar

AU - Ramadan, Ahmed A.

AU - Sessa, Salvatore

AU - Fath El-Bab, Ahmed M R

AU - Abo-Ismail, Ahmed

AU - Zecca, M.

AU - Kobayashi, Yo

AU - Takanishi, Atsuo

AU - Fujie, M.

PY - 2015/2/10

Y1 - 2015/2/10

N2 - This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.

AB - This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.

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BT - 2014 IEEE Healthcare Innovation Conference, HIC 2014

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Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter

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