Open access Jurnal SINTA 2
Analisis Kelelahan Otot pada Pengemudi Menggunakan Root Mean Square Slope Berbasis Sinyal Electromyogram
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 12 No. 6 (2025) · pp. 1327–1336
- ISSN
- 2528-6579, 2355-7699
- Published
- 2025-12-17
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Bahasa Indonesia
Kelelahan otot pada pengemudi, terutama pada otot biceps brachii yang berperan penting dalam pengendalian setir kendaraan yang berpotensi menurunkan kestabilan dan konsentrasi gerak mikro saat berkendara jarak jauh atau dalam kondisi lalu lintas yang memaksa posisi statis. Sebagian besar sistem deteksi kelelahan berfokus pada citra wajah, mata, atau pola berkendara, sedangkan kajian terhadap kelelahan otot lokal masih jarang dilakukan. Penelitian ini memosisikan deteksi kelelahan otot sebagai lapisan komplementer, bukan pengganti untuk mendukung sistem deteksi kantuk dan kelelahan mental yang telah lebih banyak dikembangkan. Penelitian ini mengusulkan pendekatan berbasis sinyal electromyogram (EMG) dengan parameter Root Mean Square (RMS) Slope untuk mendeteksi kelelahan otot biceps brachii. Data EMG diperoleh dari lima subjek pria berusia 21–22 tahun menggunakan sensor Shimmer EMG selama simulasi mengemudi mobil penumpang dengan simulator PXN V900. Sinyal EMG direkam pada frekuensi sampling 1000 Hz, kemudian diproses melalui tahapan pra-pemrosesan dan penyaringan untuk meminimalkan noise. Hasil penelitian menunjukkan bahwa sinyal EMG yang terekam konsisten berada dalam rentang milivolt sesuai dengan spesifikasi sensor. Sistem deteksi kelelahan otot mencapai akurasi 75% melalui ekstraksi fitur RMS dan klasifikasi berbasis RMS Slope. Selain itu, sistem memiliki rata-rata waktu komputasi 0,080 second per jendela 1 detik, yang cukup cepat untuk pemantauan real-time. Dengan kemampuan ini, sistem menunjukkan potensi penerapan sebagai komponen tambahan dalam meningkatkan keselamatan berkendara. Abstract Muscle fatigue in drivers, particularly in the biceps brachii which plays a key role in steering control, may compromise driving stability and fine-motor concentration during long trips or under traffic conditions requiring a static posture. Most existing fatigue detection systems focus on facial cues, eye movements, or driving patterns, while localized muscle fatigue has rarely been explored. This study positions muscle fatigue detection as a complementary layer, rather than a replacement, to support mental and drowsiness detection systems that have been more widely developed. An approach using electromyogram (EMG) signals and the Root Mean Square (RMS) Slope parameter is proposed to detect biceps brachii fatigue. EMG data were collected from five male subjects aged 21–22 years using Shimmer EMG sensors during passenger car driving simulations with a PXN V900 steering simulator. The signals were recorded at a 1000 Hz sampling rate and processed with pre-processing and filtering stages to minimize noise. Results show that the recorded EMG signals consistently remained within the millivolt range, in accordance with sensor specifications. The system achieved 75% detection accuracy through RMS feature extraction and RMS Slope classification. In addition, the average computation time was 0.080 per 1-second window, fast enough for real-time monitoring. These findings highlight the potential of integrating localized muscle fatigue detection as a complementary component to enhance driving safety.
English
Kelelahan otot pada pengemudi, terutama pada otot biceps brachii yang berperan penting dalam pengendalian setir kendaraan yang berpotensi menurunkan kestabilan dan konsentrasi gerak mikro saat berkendara jarak jauh atau dalam kondisi lalu lintas yang memaksa posisi statis. Sebagian besar sistem deteksi kelelahan berfokus pada citra wajah, mata, atau pola berkendara, sedangkan kajian terhadap kelelahan otot lokal masih jarang dilakukan. Penelitian ini memosisikan deteksi kelelahan otot sebagai lapisan komplementer, bukan pengganti untuk mendukung sistem deteksi kantuk dan kelelahan mental yang telah lebih banyak dikembangkan. Penelitian ini mengusulkan pendekatan berbasis sinyal electromyogram (EMG) dengan parameter Root Mean Square (RMS) Slope untuk mendeteksi kelelahan otot biceps brachii. Data EMG diperoleh dari lima subjek pria berusia 21–22 tahun menggunakan sensor Shimmer EMG selama simulasi mengemudi mobil penumpang dengan simulator PXN V900. Sinyal EMG direkam pada frekuensi sampling 1000 Hz, kemudian diproses melalui tahapan pra-pemrosesan dan penyaringan untuk meminimalkan noise. Hasil penelitian menunjukkan bahwa sinyal EMG yang terekam konsisten berada dalam rentang milivolt sesuai dengan spesifikasi sensor. Sistem deteksi kelelahan otot mencapai akurasi 75% melalui ekstraksi fitur RMS dan klasifikasi berbasis RMS Slope. Selain itu, sistem memiliki rata-rata waktu komputasi 0,080 second per jendela 1 detik, yang cukup cepat untuk pemantauan real-time. Dengan kemampuan ini, sistem menunjukkan potensi penerapan sebagai komponen tambahan dalam meningkatkan keselamatan berkendara. Abstract Muscle fatigue in drivers, particularly in the biceps brachii which plays a key role in steering control, may compromise driving stability and fine-motor concentration during long trips or under traffic conditions requiring a static posture. Most existing fatigue detection systems focus on facial cues, eye movements, or driving patterns, while localized muscle fatigue has rarely been explored. This study positions muscle fatigue detection as a complementary layer, rather than a replacement, to support mental and drowsiness detection systems that have been more widely developed. An approach using electromyogram (EMG) signals and the Root Mean Square (RMS) Slope parameter is proposed to detect biceps brachii fatigue. EMG data were collected from five male subjects aged 21–22 years using Shimmer EMG sensors during passenger car driving simulations with a PXN V900 steering simulator. The signals were recorded at a 1000 Hz sampling rate and processed with pre-processing and filtering stages to minimize noise. Results show that the recorded EMG signals consistently remained within the millivolt range, in accordance with sensor specifications. The system achieved 75% detection accuracy through RMS feature extraction and RMS Slope classification. In addition, the average computation time was 0.080 per 1-second window, fast enough for real-time monitoring. These findings highlight the potential of integrating localized muscle fatigue detection as a complementary component to enhance driving safety.
Keywords
Collaboration network of the authors
21 authors · 45 co-authorship links
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Barlian Henryranu Prasetio | University of Brawijaya | 3 |
| Eko Setiawan | University of Brawijaya | 2 |
| Hurriyatul Fitriyah | University of Brawijaya | 1 |
| Ryzaldi Ananda Fabiana | University of Brawijaya | 1 |
| Syahrul Chilmi | University of Brawijaya | 1 |
| Dahnial Syauqy | University of Brawijaya | 1 |
| Septiyo Budi Perkasa | University of Brawijaya | 1 |
| Rizal Maulana | University of Brawijaya | 1 |
| Dittha Ratanasari | University of Brawijaya | 1 |
| Agung Setia Budi | University of Brawijaya | 1 |
| Ragil Hadi Prasetyo | University of Brawijaya | 1 |
| Achmad Ghifari | University of Brawijaya | 1 |
| Gabriel Hizkia Marhata Simbolon | Affiliation unknown | 1 |
| Gabriel Hizkia Marhata Simbolon | Affiliation unknown | 1 |
| Teguh Surya Darma Santoso | Affiliation unknown | 1 |
| Syifa’ Hukma Shabiyya | Affiliation unknown | 1 |
| Raihan Adi Prakoso | Affiliation unknown | 1 |
| Ika Nurfarida | Affiliation unknown | 1 |
| Alifa Azwadina Fuad | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.