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Khandelwal, SiddharthaORCID iD iconorcid.org/0000-0003-4086-9221
Publications (9 of 9) Show all publications
Viteckova, S., Khandelwal, S., Kutilek, P., Krupicka, R. & Szabo, Z. (2020). Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use. Biomedical Signal Processing and Control, 55, Article ID 101643.
Open this publication in new window or tab >>Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use
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2020 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, article id 101643Article in journal (Refereed) Published
Abstract [en]

Gait symmetry has been shown to be a relevant measure for differentiating between normal and pathological gait. Although a number of symmetry methods exist, it is not clear which of these methods should be used as they have been developed using data collected from varying experimental protocols. This paper presents a comparison of state-of-the-art waveform-based symmetry methods and tests them on walking data collected from different environments. Acceleration signals collected from the ankle are used to analyse symmetry methods under different signal circumstances, such as phase shift, waveform shape difference, signal length (i.e. number of gait cycles) and gait initiation phase. The cyclogram based method is invariant to signal phase shifts, signal length and the gait initiation phase. The trend symmetry method is not affected by signal scaling and the gait initiation phase but is affected by signal length depending on the environment. Similar to the trend method, the cross-correlation symmetry method is not responsive to signal scaling and the gait initiation phase. The results of the symbolic method are not influenced by signal scaling, gait initiation and depending on the environment by the signal phase shift. From the results of the performed analysis, we recommend the trend method to gait symmetry assessment. The comparison of waveform-based symmetry methods brings new knowledge that will help in selecting an appropriate method for gait symmetry assessment under different experimental protocols. © 2019 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2020
Keywords
Gait symmetry, Trend method, Cyclogram, Symbolic method, Cross-Correlation
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-40497 (URN)10.1016/j.bspc.2019.101643 (DOI)
Note

Funding: The Czech Health Research Council (Czech Republic), Grant no. 16-28119a, “Analysis of movement disorders for the study of extrapyramidal diseases mechanism using motion capture camera systems”.

Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-10Bibliographically approved
Khandelwal, S. (2018). Gait Event Detection in the Real World. (Doctoral dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Gait Event Detection in the Real World
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Healthy gait requires a balance between various neuro-physiological systems and is considered an important indicator of a subject's physical and cognitive health status. As such, health-related applications would immensely benefit by performing long-term or continuous monitoring of subjects' gait in their natural environment and everyday lives. In contrast to stationary sensors such as motion capture systems and force plates, inertial sensors provide a good alternative for such gait analysis applications as they are miniature, cheap, mobile and can be easily integrated into wearable systems.

This thesis focuses on improving overall gait analysis using inertial sensors by providing a methodology for detecting gait events in real-world settings. Although the experimental protocols for such analysis have been restricted to only highly-controlled lab-like indoor settings; this thesis presents a new gait database that consists of data from gait activities carried out in both, indoor and outdoor environments. The thesis shows how domain knowledge about gait could be formulated and utilized to develop methods that are robust and can tackle real-world challenges. It also shows how the proposed approach can be generalized to estimate gait events from multiple body locations. Another aspect of this thesis is to demonstrate that the traditionally used temporal error metrics are not enough for presenting the overall performance of gait event detection methods. The thesis introduces how non-parametric tests can be used to complement them and provide a better overview.

The results of comparing the proposed methodology to state-of-the-art methods showed that the approach of incorporating domain knowledge into the time-frequency analysis of the signal was robust across different real-world scenarios and outperformed other methods, especially for the scenario involving variable gait speeds in outdoor settings. The methodology was also benchmarked on publicly available gait databases yielding good performance for estimating events from different body locations. To conclude, this thesis presents a road map for the development of gait analysis systems in real-world settings.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2018. p. 73
Keywords
gait analysis, gait event detection, wearable sensors, accelerometers
National Category
Signal Processing Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-36525 (URN)978-91-87045-86-8 (ISBN)978-91-87045-87-5 (ISBN)
Public defence
2018-03-14, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 10:00 (English)
Opponent
Supervisors
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-03-26Bibliographically approved
Khandelwal, S. & Wickström, N. (2018). Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations. Gait & Posture, 59, 278-285
Open this publication in new window or tab >>Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations
2018 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 59, p. 278-285Article in journal (Refereed) Published
Abstract [en]

Identifying Initial Contact events (ICE) is essential in gait analysis as they segment the walking pattern into gait cycles and facilitate the computation of other gait parameters. As such, numerous algorithms have been developed to identify ICE by placing the accelerometer at a specific body location. Simultaneously, many researchers have studied the effects of device positioning for participant or patient compliance, which is an important factor to consider especially for long-term studies in real-life settings. With the adoption of accelerometery for long-term gait analysis in daily living, current and future applications will require robust algorithms that can either autonomously adapt to changes in sensor positioning or can detect ICE from multiple sensors locations.

This study presents a novel methodology that is capable of estimating ICE from accelerometers placed at different body locations. The proposed methodology, called DK-TiFA, is based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during gait to drive the time-frequency analysis of the acceleration signal. In order to assess the performance, DK-TiFA is benchmarked on four large publicly available gait databases, consisting of a total of 613 subjects and 7 unique body locations, namely, ankle, thigh, center waist, side waist, chest, upper arm and wrist. The DK-TiFA methodology is demonstrated to achieve high accuracy and robustness for estimating ICE from data consisting of different accelerometer specifications, varying gait speeds and different environments. © 2017 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
Gait event, Inertial sensor, sensor placement, wavelet transform, domain knowledge, gait database
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-34639 (URN)10.1016/j.gaitpost.2017.07.030 (DOI)2-s2.0-85026637369 (Scopus ID)
Funder
Knowledge Foundation
Note

Funding: The Knowledge Foundation, Sweden and Promobilia Foundation, Sweden

Available from: 2017-07-21 Created: 2017-07-21 Last updated: 2018-03-26Bibliographically approved
Khandelwal, S. & Wickström, N. (2017). Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database. Gait & Posture, 51, 84-90
Open this publication in new window or tab >>Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database
2017 (English)In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 51, p. 84-90Article in journal (Refereed) Published
Abstract [en]

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings.

This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios. © 2016 Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2017
Keywords
gait events, gait event detection, accelerometer, inertial sensor, gait database, gait dataset, Heel Strike, Toe Off
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-32110 (URN)10.1016/j.gaitpost.2016.09.023 (DOI)000390463000015 ()27736735 (PubMedID)2-s2.0-84991511975 (Scopus ID)
Funder
Knowledge Foundation
Note

This study was supported in part by the Knowledge Foundation, Sweden.

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-03-26Bibliographically approved
Bentes, J., Khandelwal, S., Carlsson, H., Kärrman, M., Svensson, T. & Wickström, N. (2017). Novel System Architecture for Online Gait Analysis. In: : . Paper presented at 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, South Korea, July 11-15, 2017.
Open this publication in new window or tab >>Novel System Architecture for Online Gait Analysis
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2017 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Although wearable devices can be used to perform continuous gait analysis in daily life, existing platforms only support short-term analysis in quasi-controlled environments. This paper proposes a novel system architecture that is designed for long-term, online gait analysis in free-living environments. Various aspects related to the feasibility and scalability of the proposed system are presented.

Keywords
Integrated wearable and portable systems, Physiological monitoring - Modeling and analysis, Physiological monitoring - Novel methods
National Category
Communication Systems
Identifiers
urn:nbn:se:hh:diva-34302 (URN)
Conference
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, South Korea, July 11-15, 2017
Available from: 2017-06-22 Created: 2017-06-22 Last updated: 2018-10-31Bibliographically approved
Khandelwal, S. & Wickström, N. (2016). Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis. IEEE transactions on neural systems and rehabilitation engineering, 24(12), 1363-1372
Open this publication in new window or tab >>Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge into Time-Frequency Analysis
2016 (English)In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 24, no 12, p. 1363-1372Article in journal (Refereed) Published
Abstract [en]

Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans’ natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from longterm accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93,600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments. © Copyright 2016 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2016
Keywords
accelerometer, gait analysis, inertial sensors, morlet, principles of gait, stride parameters, wavelet transform
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-30468 (URN)10.1109/TNSRE.2016.2536278 (DOI)000390559600010 ()26955043 (PubMedID)2-s2.0-85006253692 (Scopus ID)
Available from: 2016-03-04 Created: 2016-03-04 Last updated: 2018-03-26Bibliographically approved
Khandelwal, S. & Wickström, N. (2014). Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications. In: 13th International Symposium on 3D Analysis of Human Movement: 14–17 July, 2014, Lausanne, Switzerland. Paper presented at 13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland (pp. 151-154).
Open this publication in new window or tab >>Detecting Gait Events from Outdoor Accelerometer Data for Long-term and Continuous Monitoring Applications
2014 (English)In: 13th International Symposium on 3D Analysis of Human Movement: 14–17 July, 2014, Lausanne, Switzerland, 2014, , p. 4p. 151-154Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Detecting gait events is the key to many gait analysis applications which would immensely benefit if the analysis could be carried out using wearable sensors in uncontrolled outdoor environments, enabling continuous monitoring and long-term analysis. This would allow exploring new frontiers in gait analysis by facilitating the availability of more data and empower individuals, especially patients, to avail the benefits of gait analysis in their everyday lives. Previous gait event detection algorithms impose many restrictions as they have been developed from data collected incontrolled, indoor environments. This paper proposes a robust algorithm that utilizes a priori knowledge of gait in conjunction with continuous wavelet transform analysis, to accurately identify heel strike and toe off, from noisy accelerometer signals collected during indoor and outdoor walking. The accuracy of the algorithm is evaluated by using footswitches that are considered as ground truth and the results are compared with another recently published algorithm.

Publisher
p. 4
Keywords
gait event detection, gait event identification, accelerometer, outdoor walking, wavelet transform, continuous monitoring, long term applications, overground walking
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-26174 (URN)9782880748562 (ISBN)
Conference
13th International Symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14–17 July, 2014, Lausanne, Switzerland
Projects
HMC2
Available from: 2014-07-23 Created: 2014-07-23 Last updated: 2016-03-09Bibliographically approved
Khandelwal, S. & Wickström, N. (2014). Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis. In: Harald Loose, Guy Plantier, Tanja Schultz, Ana Fred & Hugo Gamboa (Ed.), BIOSIGNALS 2014: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. Paper presented at 7th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2014), Angers, France, March 3-6, 2014 (pp. 197-204). [S.l.]: SciTePress
Open this publication in new window or tab >>Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis
2014 (English)In: BIOSIGNALS 2014: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing / [ed] Harald Loose, Guy Plantier, Tanja Schultz, Ana Fred & Hugo Gamboa, [S.l.]: SciTePress, 2014, p. 197-204Conference paper, Published paper (Refereed)
Abstract [en]

Many gait analysis applications involve long-term or continuous monitoring which require gait measurements to be taken outdoors. Wearable inertial sensors like accelerometers have become popular for such applications as they are miniature, low-powered and inexpensive but with the drawback that they are prone to noise and require robust algorithms for precise identification of gait events. However, most gait event detection algorithms have been developed by simulating physical world environments inside controlled laboratories. In this paper, we propose a novel algorithm that robustly and efficiently identifies gait events from accelerometer signals collected during both, indoor and outdoor walking of healthy subjects. The proposed method makes adept use of prior knowledge of walking gait characteristics, referred to as expert knowledge, in conjunction with continuous wavelet transform analysis to detect gait events of heel strike and toe off. It was observed that in comparison to indoor, the outdoor walking acceleration signals were of poorer quality and highly corrupted with noise. The proposed algorithm presents an automated way to effectively analyze such noisy signals in order to identify gait events.

Place, publisher, year, edition, pages
[S.l.]: SciTePress, 2014
Keywords
Gait Event Detection, Gait Event Identification, Wavelet Analysis, Accelerometers, Outdoor Walking, Continuous Wavelet Transform, Inertial Sensors, Expert Knowledge
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-24396 (URN)10.5220/0004799801970204 (DOI)2-s2.0-84902342535 (Scopus ID)978-989-758-011-6 (ISBN)
Conference
7th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2014), Angers, France, March 3-6, 2014
Available from: 2014-01-18 Created: 2014-01-18 Last updated: 2016-03-09Bibliographically approved
Khandelwal, S. & Chevallereau, C. (2013). Estimation of the Trunk Attitude of a Humanoid by Data Fusion of Inertial Sensors and Joint Encoders. In: Kenneth J. Waldron, Mohammad O. Tokhi & Gurvinder S. Virk (Ed.), Nature-Inspired Mobile Robotics: . Paper presented at The 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2013), Sydney, Australia, 14-17 July, 2013 (pp. 822-830). Singapore: World Scientific
Open this publication in new window or tab >>Estimation of the Trunk Attitude of a Humanoid by Data Fusion of Inertial Sensors and Joint Encoders
2013 (English)In: Nature-Inspired Mobile Robotics / [ed] Kenneth J. Waldron, Mohammad O. Tokhi & Gurvinder S. Virk, Singapore: World Scientific, 2013, p. 822-830Conference paper, Published paper (Refereed)
Abstract [en]

The major problem associated with the walking of humanoid robots is to main- tain its dynamic equilibrium while walking. To achieve this one must detect gait instability during walking to apply proper fall avoidance schemes and bring back the robot into stable equilibrium. A good approach to detect gait insta- bility is to study the evolution of the attitude of the humanoid's trunk. Most attitude estimation techniques involve using the information from inertial sen- sors positioned at the trunk. However, inertial sensors like accelerometer and gyro are highly prone to noise which lead to poor attitude estimates that can cause false fall detections and falsely trigger fall avoidance schemes. In this paper we present a novel way to access the information from joint encoders present in the legs and fuse it with the information from inertial sensors to provide a highly improved attitude estimate during humanoid walk. Also if the joint encoders' attitude measure is compared separately with the IMU's atti- tude estimate, then it is observed that they are different when there is a change of contact between the stance leg and the ground. This may be used to detect a loss of contact and can be verified by the information from force sensors present at the feet of the robot. The propositions are validated by experiments performed on humanoid robot NAO. Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd.

Place, publisher, year, edition, pages
Singapore: World Scientific, 2013
Keywords
Humanoid walking, Dynamic equilibrium, Attitude estimation, Instability detection, Sensor fusion, Inertial sensors, Joint encoders
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-23363 (URN)10.1142/9789814525534_0101 (DOI)000337122900101 ()2-s2.0-84891358437 (Scopus ID)978-981-4525-52-7 (ISBN)978-981-4525-54-1 (ISBN)
Conference
The 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2013), Sydney, Australia, 14-17 July, 2013
Available from: 2013-08-17 Created: 2013-08-17 Last updated: 2018-01-16Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4086-9221

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