{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:42:38Z","timestamp":1780598558676,"version":"3.54.1"},"reference-count":35,"publisher":"Cambridge University Press (CUP)","issue":"7","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger\u2019s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0263574721001533_inline1.png\"\/><jats:tex-math>$^\\circ$<\/jats:tex-math><\/jats:alternatives><\/jats:inline-formula>and 3<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0263574721001533_inline2.png\"\/><jats:tex-math>$^\\circ$<\/jats:tex-math><\/jats:alternatives><\/jats:inline-formula>, sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator\u2019s behavior in free and constrained states using the sensing system\u2019s data. Thus, parametric system identification methods, artificial neural network\u2014multilayer perceptron (ANN-MLP), and artificial neural network\u2014radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (&lt;1<jats:inline-formula><jats:alternatives><jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0263574721001533_inline3.png\"\/><jats:tex-math>$^\\circ$<\/jats:tex-math><\/jats:alternatives><\/jats:inline-formula>). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.<\/jats:p>","DOI":"10.1017\/s0263574721001533","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T08:32:50Z","timestamp":1635409970000},"page":"2091-2111","source":"Crossref","is-referenced-by-count":19,"title":["Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation"],"prefix":"10.1017","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5747-2995","authenticated-orcid":false,"given":"Shokoufeh","family":"Davarzani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali","family":"Ahmadi-Pajouh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Ghafarirad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"S0263574721001533_ref15","doi-asserted-by":"publisher","DOI":"10.12928\/telkomnika.v16i2.9020"},{"key":"S0263574721001533_ref7","doi-asserted-by":"publisher","DOI":"10.2991\/eame-17.2017.45"},{"key":"S0263574721001533_ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.08.021"},{"key":"S0263574721001533_ref19","doi-asserted-by":"publisher","DOI":"10.3390\/s18051545"},{"key":"S0263574721001533_ref30","first-page":"20","article-title":"Predictive abilities of bayesian regularization and levenberg\u2013marquardt algorithms in artificial neural networks: A comparative empirical study on social data","volume":"21","author":"Kayri","year":"2016","journal-title":"Math. 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