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Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0001-7048-0108
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).ORCID iD: 0000-0002-8028-3607
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0002-0699-3889
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, p. 986-986Article in journal (Refereed) Published
Abstract [en]

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous. 

Place, publisher, year, edition, pages
2022. Vol. 12, no 3, p. 986-986
Keywords [en]
prosthetic socket; mechatronics-twin; biomechanical modeling and simulation; Stewart manipulator; stochastic dynamic process; wearable sensor; Autoencoder (AE); Bayesian Inference (BI); Hidden Markov Model (HMM); data classification; anomaly detection
National Category
Bioengineering Equipment Other Mechanical Engineering Robotics Human Computer Interaction Information Systems
Research subject
Machine Design; Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-309096DOI: 10.3390/app12030986ISI: 000754916500001Scopus ID: 2-s2.0-85123060618OAI: oai:DiVA.org:kth-309096DiVA, id: diva2:1639368
Projects
SocketSense
Funder
EU, Horizon 2020, 825429
Note

QC 20220223

Available from: 2022-02-21 Created: 2022-02-21 Last updated: 2022-06-25Bibliographically approved

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Chen, DeJiuSu, PengOttikkutti, SuranjanTahmasebi, Kaveh Nazem

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Chen, DeJiuSu, PengOttikkutti, SuranjanVartholomeos, PanagiotisTahmasebi, Kaveh Nazem
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MechatronicsMachine Design (Dept.)Embedded Control Systems
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Applied Sciences
Bioengineering EquipmentOther Mechanical EngineeringRoboticsHuman Computer InteractionInformation Systems

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