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All Journal TEKNIK INFORMATIKA Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI IJIS - Indonesian Journal On Information System JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Paedagogy Journal of Innovation Information Technology and Application (JINITA) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Society: Jurnal Pengabdian Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Sistem Informasi dan Bisnis Cerdas Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Pengabdian Bisnis dan Akuntansi Soedirman Jurnal Informatika Polinema (JIP) Nusantara Journal of Artificial Intelligence and Information Systems
Amalia Beladinna Arifa
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 1 
Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU Andi Prademon Yunus; Amalia Beladinna Arifa; Yit Hong Choo
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.41027

Abstract

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention
Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU Yunus, Andi Prademon; Arifa, Amalia Beladinna; Choo, Yit Hong
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.41027

Abstract

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention
Co-Authors Abdul Hafiidh Priyanto Agi Prasetiadi Alika, Shintia Dwi Amalia, Risa Riski Ananda Rifkiy Hasan Andi Prademon Yunus Annisa Nugraheni Aprianti Ika Larasati Asti Dwi Sripamuji Atika Ratna Dewi Aufaresa, Juvandio Aulia Burhanuddin Aulia Romadloni Nur Indarti Auliya Burhanuddin Choo, Yit Hong Dodi Zulherman Elisabeth Angeline W B Gladi Pawestri Utami Habibah Ratna Fadhila Islami Hana Hananta, Arnelka Hapsari Warih Utami Herdiesel Santoso Hidayah, Annisa Nur I Made Wirawan Imada Ramadhanti Ipam Fuaddina Adam Julian Saputra Julian Saputra Muhammad Genta Ari Shandi Muhammad Zidny Naf'an Nasrulloh, Iffan Ahmad Novian Adi Prasetyo Paradise, Paradise Pigi, Salman Pradana Ananda Raharja Ramadhani, Rima Dias Ramadhanti, Imada Ridho Ananda Rifki Adhitama, Rifki Rindu Hafil Muhammadi Risa Riski Amalia Rokhmawati, Naeli Fitria Sheren Afryan Tiastama Sidiq, Muhammad Fajar Slamet Wibawanto Sripamuji, Asti Dwi Testandy, Zaky Hanif Theo Felix Harianto Purba Tri Ginanjar Laksana Trihastuti Yuniati Utami, Annisaa Vania Noverina Vico Meylana Eka Putra Wahyu Adi Prabowo Wibowo, Fahrudin Mukti Yit Hong Choo Yogo Dwi Prasetyo Yohani Setiya Rafika Nur