A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors
Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefi...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/260 |
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| author | Lucia Palazzo Vladimiro Suglia Sabrina Grieco Domenico Buongiorno Antonio Brunetti Leonarda Carnimeo Federica Amitrano Armando Coccia Gaetano Pagano Giovanni D’Addio Vitoantonio Bevilacqua |
| author_facet | Lucia Palazzo Vladimiro Suglia Sabrina Grieco Domenico Buongiorno Antonio Brunetti Leonarda Carnimeo Federica Amitrano Armando Coccia Gaetano Pagano Giovanni D’Addio Vitoantonio Bevilacqua |
| author_sort | Lucia Palazzo |
| collection | DOAJ |
| description | Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time. |
| format | Article |
| id | doaj-art-50ef11f194794436a8ce62ae05d551ea |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-50ef11f194794436a8ce62ae05d551ea2025-01-10T13:21:23ZengMDPI AGSensors1424-82202025-01-0125126010.3390/s25010260A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial SensorsLucia Palazzo0Vladimiro Suglia1Sabrina Grieco2Domenico Buongiorno3Antonio Brunetti4Leonarda Carnimeo5Federica Amitrano6Armando Coccia7Gaetano Pagano8Giovanni D’Addio9Vitoantonio Bevilacqua10Bioengineering Unit of Bari, Istituti Clinici Scientifici Maugeri IRCCS, Via Generale Bellomo, 73/75, 70124 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyBioengineering Unit of Telese Terme, Istituti Clinici Scientifici Maugeri IRCCS, Via Bagni Vecchi, 1, 82037 Telese Terme, ItalyBioengineering Unit of Telese Terme, Istituti Clinici Scientifici Maugeri IRCCS, Via Bagni Vecchi, 1, 82037 Telese Terme, ItalyBioengineering Unit of Bari, Istituti Clinici Scientifici Maugeri IRCCS, Via Generale Bellomo, 73/75, 70124 Bari, ItalyBioengineering Unit of Bari, Istituti Clinici Scientifici Maugeri IRCCS, Via Generale Bellomo, 73/75, 70124 Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, ItalyAbnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time.https://www.mdpi.com/1424-8220/25/1/260gait recognitiongait disordersinertial measurement unitsdeep learningconvolutional neural networkrehabilitation |
| spellingShingle | Lucia Palazzo Vladimiro Suglia Sabrina Grieco Domenico Buongiorno Antonio Brunetti Leonarda Carnimeo Federica Amitrano Armando Coccia Gaetano Pagano Giovanni D’Addio Vitoantonio Bevilacqua A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors Sensors gait recognition gait disorders inertial measurement units deep learning convolutional neural network rehabilitation |
| title | A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors |
| title_full | A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors |
| title_fullStr | A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors |
| title_full_unstemmed | A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors |
| title_short | A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors |
| title_sort | deep learning based framework oriented to pathological gait recognition with inertial sensors |
| topic | gait recognition gait disorders inertial measurement units deep learning convolutional neural network rehabilitation |
| url | https://www.mdpi.com/1424-8220/25/1/260 |
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