Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces
The research improves mechanical systems by using wearable sensor-based Augmented Reality (AR) interfaces for better Human-Machine Interaction (HCI). Industrial AR systems currently face problems created by their static programming methods along with delayed responsiveness and restricted sensor coll...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Measurement: Sensors |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917425000741 |
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| author | K. Balamurugan G. Sudhakar Kavin Francis Xavier N. Bharathiraja Gaganpreet Kaur |
| author_facet | K. Balamurugan G. Sudhakar Kavin Francis Xavier N. Bharathiraja Gaganpreet Kaur |
| author_sort | K. Balamurugan |
| collection | DOAJ |
| description | The research improves mechanical systems by using wearable sensor-based Augmented Reality (AR) interfaces for better Human-Machine Interaction (HCI). Industrial AR systems currently face problems created by their static programming methods along with delayed responsiveness and restricted sensor collectability and insufficient wireless throughput that results in system inefficiency and elevated stress on users. A new wearable AR system using gloves with haptic feedback and flex sensors with Inertial Measurement Units provides precise gesture-control while displaying real-time contextual information. The dynamic gesture recognition system uses Random Forest as its lightweight machine learning model to achieve 93.4 % accuracy in mapping gestures to command sequences which represents a 14.6 % enhancement above conventional static models. The system leverages Edge Computing for low-latency processing (average latency <47 ms) and cloud-based analytics for predictive maintenance insights. The proposed setup demonstrated an enhanced industrial performance in a simulated environment through error reduction by 22.3 % along with a 31.1 % increase in task speed and a 27.8 % improvement in situational awareness recorded through NASA-TLX cognitive load evaluations. Findings prove that the system fills fundamental weaknesses with current AR-assisted industrial HCI systems by providing automatic adaptation features along with improved safety measures and precise operational capability. |
| format | Article |
| id | doaj-art-97f5657ec6f94824a22cf7ab52aec77a |
| institution | OA Journals |
| issn | 2665-9174 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj-art-97f5657ec6f94824a22cf7ab52aec77a2025-08-20T02:26:09ZengElsevierMeasurement: Sensors2665-91742025-06-013910188010.1016/j.measen.2025.101880Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfacesK. Balamurugan0G. Sudhakar1Kavin Francis Xavier2N. Bharathiraja3Gaganpreet Kaur4Faculty of Mechanical Engineering, SRM Madurai College for Engineering and Technology, Pottapalayam, 630612, TN, IndiaDepartment of CSE, Sri Sai Ranganathan Engineering College, Coimbatore, Tamil Nadu, IndiaInstrumentation Engineer, M/S Muscat Engineering Consultancy Pvt. Ltd, Trichy, 620001, Tamil Nadu, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Corresponding author.The research improves mechanical systems by using wearable sensor-based Augmented Reality (AR) interfaces for better Human-Machine Interaction (HCI). Industrial AR systems currently face problems created by their static programming methods along with delayed responsiveness and restricted sensor collectability and insufficient wireless throughput that results in system inefficiency and elevated stress on users. A new wearable AR system using gloves with haptic feedback and flex sensors with Inertial Measurement Units provides precise gesture-control while displaying real-time contextual information. The dynamic gesture recognition system uses Random Forest as its lightweight machine learning model to achieve 93.4 % accuracy in mapping gestures to command sequences which represents a 14.6 % enhancement above conventional static models. The system leverages Edge Computing for low-latency processing (average latency <47 ms) and cloud-based analytics for predictive maintenance insights. The proposed setup demonstrated an enhanced industrial performance in a simulated environment through error reduction by 22.3 % along with a 31.1 % increase in task speed and a 27.8 % improvement in situational awareness recorded through NASA-TLX cognitive load evaluations. Findings prove that the system fills fundamental weaknesses with current AR-assisted industrial HCI systems by providing automatic adaptation features along with improved safety measures and precise operational capability.http://www.sciencedirect.com/science/article/pii/S2665917425000741Ergonomic wearablesMechanical systemsAugmented realityOptimizeHuman-machine interactionInternet of things |
| spellingShingle | K. Balamurugan G. Sudhakar Kavin Francis Xavier N. Bharathiraja Gaganpreet Kaur Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces Measurement: Sensors Ergonomic wearables Mechanical systems Augmented reality Optimize Human-machine interaction Internet of things |
| title | Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| title_full | Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| title_fullStr | Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| title_full_unstemmed | Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| title_short | Human-machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| title_sort | human machine interaction in mechanical systems through sensor enabled wearable augmented reality interfaces |
| topic | Ergonomic wearables Mechanical systems Augmented reality Optimize Human-machine interaction Internet of things |
| url | http://www.sciencedirect.com/science/article/pii/S2665917425000741 |
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