Making Sense of Complex Sensor Data Streams
This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specif...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/12/1391 |
| _version_ | 1797530685041278976 |
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| author | Rongrong Liu Birgitta Dresp-Langley |
| author_facet | Rongrong Liu Birgitta Dresp-Langley |
| author_sort | Rongrong Liu |
| collection | DOAJ |
| description | This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image-guided task performed in a real-world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task-specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio-temporal grip force profiles allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable drawing meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability. |
| first_indexed | 2024-03-10T10:33:29Z |
| format | Article |
| id | doaj.art-b5bc3faef82a4378ac55dd555b11a596 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:33:29Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b5bc3faef82a4378ac55dd555b11a5962023-11-21T23:30:06ZengMDPI AGElectronics2079-92922021-06-011012139110.3390/electronics10121391Making Sense of Complex Sensor Data StreamsRongrong Liu0Birgitta Dresp-Langley1ICube Lab, Robotics Department, Strasbourg University, 67085 Strasbourg, FranceICube Lab, UMR 7357 Centre National de la Recherche Scientifique CNRS, Strasbourg University, 67085 Strasbourg, FranceThis concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image-guided task performed in a real-world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task-specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio-temporal grip force profiles allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable drawing meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability.https://www.mdpi.com/2079-9292/10/12/1391wireless technologywearable biosensorgrip force datastatistical analysis |
| spellingShingle | Rongrong Liu Birgitta Dresp-Langley Making Sense of Complex Sensor Data Streams Electronics wireless technology wearable biosensor grip force data statistical analysis |
| title | Making Sense of Complex Sensor Data Streams |
| title_full | Making Sense of Complex Sensor Data Streams |
| title_fullStr | Making Sense of Complex Sensor Data Streams |
| title_full_unstemmed | Making Sense of Complex Sensor Data Streams |
| title_short | Making Sense of Complex Sensor Data Streams |
| title_sort | making sense of complex sensor data streams |
| topic | wireless technology wearable biosensor grip force data statistical analysis |
| url | https://www.mdpi.com/2079-9292/10/12/1391 |
| work_keys_str_mv | AT rongrongliu makingsenseofcomplexsensordatastreams AT birgittadresplangley makingsenseofcomplexsensordatastreams |