Performance Analysis of Human Activity Article Swipe
Rutuja Mhaiskar
,
Dhandapani Vaithiyanathan
,
Preeti Verma
,
Baljit Kaur
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/itmconf/20235605006
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/itmconf/20235605006
This project aims to develop an AI-powered gym assistant using Jupyter Notebook and MediaPipe, a popular computer vision library, to count the repetitions of three joint exercises: curls, squats, and sit-ups. The system will provide real-time feedback and monitoring, allowing users to track their progress and improve performance. The proposed method utilizes MediaPipe, which offers pre-trained machine-learning models for human pose estimation and hand tracking. These models will accurately detect and track critical body joints and hand movements during the exercises. The system will then analyze the detected poses to identify the repetitions of each activity based on predefined movement patterns and pose thresholds. Jupyter Notebook will be used as the development environment for coding and testing the system. Python programming language and MediaPipe’s Python API will be employed to implement the pose estimation and repetition counting algorithms. The system will also incorporate a user-friendly interface, allowing users to interact with the gym assistant and receive feedback on their exercise performance. The completed project will provide an AI-powered gym assistant that can accurately count the repetitions of curls, squats, and sit-ups in real-time. Additionally, this project will contribute to the advancement of the field of fitness technology by showcasing the potential of combining computer vision and artificial intelligence techniques for gym monitoring and performance tracking. The results of this project have the potential to benefit fitness enthusiasts, trainers, and researchers alike, providing contributions to the field of fitness technology.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/itmconf/20235605006
- https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05006.pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385705524
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385705524Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/itmconf/20235605006Digital Object Identifier
- Title
-
Performance Analysis of Human ActivityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Rutuja Mhaiskar, Dhandapani Vaithiyanathan, Preeti Verma, Baljit KaurList of authors in order
- Landing page
-
https://doi.org/10.1051/itmconf/20235605006Publisher landing page
- PDF URL
-
https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05006.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05006.pdfDirect OA link when available
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Python (programming language), Computer science, Artificial intelligence, Human–computer interaction, Machine learning, Tracking system, Simulation, Multimedia, Operating system, Kalman filterTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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9Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 31, 48, 81, 138, 161, 215 |
| abstract_inverted_index.and | 12, 29, 37, 45, 62, 70, 75, 101, 115, 122, 134, 154, 179, 205, 212, 228 |
| abstract_inverted_index.can | 171 |
| abstract_inverted_index.for | 58, 113, 209 |
| abstract_inverted_index.gym | 7, 152, 168, 210 |
| abstract_inverted_index.the | 21, 79, 86, 91, 110, 117, 131, 151, 174, 189, 192, 199, 221, 234 |
| abstract_inverted_index.This | 0 |
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| abstract_inverted_index.body | 73 |
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| abstract_inverted_index.hand | 63, 76 |
| abstract_inverted_index.have | 220 |
| abstract_inverted_index.pose | 60, 102, 132 |
| abstract_inverted_index.that | 170 |
| abstract_inverted_index.then | 84 |
| abstract_inverted_index.this | 184, 218 |
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| abstract_inverted_index.will | 33, 67, 83, 106, 126, 140, 164, 186 |
| abstract_inverted_index.with | 150 |
| abstract_inverted_index.These | 65 |
| abstract_inverted_index.based | 96 |
| abstract_inverted_index.count | 20, 173 |
| abstract_inverted_index.field | 193, 235 |
| abstract_inverted_index.human | 59 |
| abstract_inverted_index.joint | 25 |
| abstract_inverted_index.poses | 88 |
| abstract_inverted_index.their | 43, 158 |
| abstract_inverted_index.three | 24 |
| abstract_inverted_index.track | 42, 71 |
| abstract_inverted_index.users | 40, 147 |
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| abstract_inverted_index.which | 53 |
| abstract_inverted_index.Python | 119, 124 |
| abstract_inverted_index.alike, | 230 |
| abstract_inverted_index.coding | 114 |
| abstract_inverted_index.curls, | 27, 177 |
| abstract_inverted_index.detect | 69 |
| abstract_inverted_index.during | 78 |
| abstract_inverted_index.joints | 74 |
| abstract_inverted_index.method | 50 |
| abstract_inverted_index.models | 57, 66 |
| abstract_inverted_index.offers | 54 |
| abstract_inverted_index.system | 32, 82, 139 |
| abstract_inverted_index.vision | 17, 204 |
| abstract_inverted_index.Jupyter | 10, 104 |
| abstract_inverted_index.analyze | 85 |
| abstract_inverted_index.benefit | 224 |
| abstract_inverted_index.develop | 4 |
| abstract_inverted_index.fitness | 195, 225, 237 |
| abstract_inverted_index.improve | 46 |
| abstract_inverted_index.popular | 15 |
| abstract_inverted_index.project | 1, 163, 185, 219 |
| abstract_inverted_index.provide | 34, 165 |
| abstract_inverted_index.receive | 155 |
| abstract_inverted_index.results | 216 |
| abstract_inverted_index.sit-ups | 180 |
| abstract_inverted_index.squats, | 28, 178 |
| abstract_inverted_index.system. | 118 |
| abstract_inverted_index.testing | 116 |
| abstract_inverted_index.Notebook | 11, 105 |
| abstract_inverted_index.activity | 95 |
| abstract_inverted_index.allowing | 39, 146 |
| abstract_inverted_index.computer | 16, 203 |
| abstract_inverted_index.counting | 136 |
| abstract_inverted_index.critical | 72 |
| abstract_inverted_index.detected | 87 |
| abstract_inverted_index.employed | 128 |
| abstract_inverted_index.exercise | 159 |
| abstract_inverted_index.feedback | 36, 156 |
| abstract_inverted_index.identify | 90 |
| abstract_inverted_index.interact | 149 |
| abstract_inverted_index.language | 121 |
| abstract_inverted_index.library, | 18 |
| abstract_inverted_index.movement | 99 |
| abstract_inverted_index.patterns | 100 |
| abstract_inverted_index.progress | 44 |
| abstract_inverted_index.proposed | 49 |
| abstract_inverted_index.sit-ups. | 30 |
| abstract_inverted_index.utilizes | 51 |
| abstract_inverted_index.assistant | 8, 153, 169 |
| abstract_inverted_index.combining | 202 |
| abstract_inverted_index.completed | 162 |
| abstract_inverted_index.implement | 130 |
| abstract_inverted_index.movements | 77 |
| abstract_inverted_index.potential | 200, 222 |
| abstract_inverted_index.providing | 231 |
| abstract_inverted_index.real-time | 35 |
| abstract_inverted_index.tracking. | 64, 214 |
| abstract_inverted_index.trainers, | 227 |
| abstract_inverted_index.AI-powered | 6, 167 |
| abstract_inverted_index.MediaPipe, | 13, 52 |
| abstract_inverted_index.accurately | 68, 172 |
| abstract_inverted_index.artificial | 206 |
| abstract_inverted_index.contribute | 187 |
| abstract_inverted_index.estimation | 61, 133 |
| abstract_inverted_index.exercises. | 80 |
| abstract_inverted_index.exercises: | 26 |
| abstract_inverted_index.interface, | 145 |
| abstract_inverted_index.monitoring | 211 |
| abstract_inverted_index.predefined | 98 |
| abstract_inverted_index.real-time. | 182 |
| abstract_inverted_index.repetition | 135 |
| abstract_inverted_index.showcasing | 198 |
| abstract_inverted_index.techniques | 208 |
| abstract_inverted_index.technology | 196 |
| abstract_inverted_index.advancement | 190 |
| abstract_inverted_index.algorithms. | 137 |
| abstract_inverted_index.development | 111 |
| abstract_inverted_index.environment | 112 |
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| abstract_inverted_index.monitoring, | 38 |
| abstract_inverted_index.performance | 213 |
| abstract_inverted_index.pre-trained | 55 |
| abstract_inverted_index.programming | 120 |
| abstract_inverted_index.repetitions | 22, 92, 175 |
| abstract_inverted_index.researchers | 229 |
| abstract_inverted_index.technology. | 238 |
| abstract_inverted_index.thresholds. | 103 |
| abstract_inverted_index.enthusiasts, | 226 |
| abstract_inverted_index.intelligence | 207 |
| abstract_inverted_index.performance. | 47, 160 |
| abstract_inverted_index.Additionally, | 183 |
| abstract_inverted_index.MediaPipe’s | 123 |
| abstract_inverted_index.contributions | 232 |
| abstract_inverted_index.user-friendly | 144 |
| abstract_inverted_index.machine-learning | 56 |
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| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.68610759 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |