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IMU Sensör Tabanlı Genişletilebilir Düşme Tespit Sistemi Tasarımı

Yıl 2025, Cilt: 15 Sayı: 1, 115 - 127, 30.06.2025
https://doi.org/10.54370/ordubtd.1658926

Öz

Düşmeler ve sonuçları, çeşitli yaş gruplarındaki bireyleri etkileyen önemli sağlık sorunlarını ortaya çıkartır. Yaşlanan bireyler genellikle daha güçsüz, daha dengesizdir ve daha yavaş tepki verirler, bu da düşme ve yaralanma olasılıklarını artırır. Düşme ciddi bir endişe kaynağıdır, hareket ve yaşam kalitesi üzerinde önemli bir etkiye sahiptir. Ayrıca dünya çapında sağlık sistemleri üzerinde önemli bir finansal etkiye sahiptir. Bir düşmenin etkisi, küçük morluklar, yaralanmalar, hayatı zorlaştıran kırıklar ve hatta ölümcül olabilen durumlara kadar değişebilir. Bu nedenlerle yaşlı ve engelli kişilerin aktivitelerinin sürekli olarak izlenmesi tele-tıbbın temel amaçlarından biri haline gelmiş ve giyilebilir cihazlar yaygınlaşmıştır. Bu çalışmanın temel amacı, düşme durumlarının hassas ve otomatik olarak algılanmasına ve izlenmesine imkan tanıyan bir sistem geliştirmektir. Bu yaklaşım, bakıcıları veya tıp doktorlarını hızlı bir şekilde bilgilendirmek için zamanında uyarılar ve bildirimler üretecektir. Çalışmada oluşturulan sistem, geliştirilebilir özellikte olup çok sayıda sensör eklenebilmektedir. Hastanın üzerine yerleştirilen IMU sensörlerden, Raspberry Pi'ye aktarılan veriler yazılımla değerlendirilmektedir. Normal duruş seviyeleri olarak belirlenen değerlerden ani değişiklikler meydana geldiğinde düşme algısı oluşturulur. Eğilme ve düşmeler ayrıştırılır. Bu durum göz önüne alınarak çeşitli düşme varyasyonları tespit edilir.

Kaynakça

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  • Cedeno-Moreno, R., Malagon-Barillas, D. L., Morales-Hernandez, L. A., Gonzalez-Hernandez, M. P., & Cruz-Albarran, I. A. (2024). Computer vision system based on the analysis of gait features for fall risk assessment in elderly people. Applied Sciences, 14(9),3867, https://doi.org/10.3390/app14093867
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IMU Sensor Based Expandable Fall Detection System Design

Yıl 2025, Cilt: 15 Sayı: 1, 115 - 127, 30.06.2025
https://doi.org/10.54370/ordubtd.1658926

Öz

Falls and their consequences pose significant health problems affecting individuals of various age groups. Aging individuals are generally weaker, less stable, and slower to react, increasing the likelihood of falls and injuries. Falls are a serious concern, have a significant impact on mobility and quality of life. They also have a significant financial impact on healthcare systems worldwide. The effects of a fall can range from minor bruises, injuries, life-threatening fractures and even fatal conditions. For these reasons, continuous monitoring of the activities of elderly and disabled people has become one of the main goals of telemedicine, and wearable devices have become widespread. The main goal of this study is to develop a system that allows for the precise and automatic detection and monitoring of falls. This approach will generate timely alerts and notifications to quickly inform caregivers or medical doctors. The system created in the study is expandable and can add a large number of sensors. The data transferred from the IMU sensors placed on the patient to the Raspberry Pi is evaluated by software. A fall perception is created when sudden changes occur from the values determined as normal posture levels. Bending and falling are separated. Taking this into account, various falling variations are detected.

Etik Beyan

There are no ethical issues related to the publication of this article

Destekleyen Kurum

This study was supported by TUBITAK (2209/A).

Kaynakça

  • Abdulmalek, S., Nasir, A., Jabbar, W. A., Almuhaya, M. A. M., Bairagi, A.K., Khan, M. A., & Kee, S. H. (2022). IoT-based healthcare-monitoring system towards improving quality of life: A review. Healthcare (Basel). 10(10), 1993. https://doi.org/10.3390/healthcare10101993
  • Archibald, D. A., Kannan, G., Mensah, S., Kishore, R., Sonia, M., Alice, M., & Sampson, A. (2024). Fall prevention and monitoring device for the aged-on admission. 2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST), Accra, Ghana, pp.1-5, https://doi.org/10.1109/ICAST61769.2024.10856501
  • Arnaoutoglou, D. G., Dedemadis, D., Kyriakou, A. A., Katsimentes, S., Grekidis, A., Menychtas, D., Aggelousis, N., Sirakoulis, G. C., & Kyriacou, G.A. (2024). Acceleration-based low-cost CW radar system for real-time elderly fall detection. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 8(2), 102-112, https://doi.org/10.1109/JERM.2024.3368688
  • Campanella, S., Alnasef, A., Falaschetti, L., Belli, A., Pierleoni, P., & Palma, L. (2024). A novel embedded deep learning wearable sensor for fall detection. IEEE Sensors Journal, 24(9), 15219-15229, 2024, https://doi.org/10.1109/JSEN.2024.3375603
  • Cedeno-Moreno, R., Malagon-Barillas, D. L., Morales-Hernandez, L. A., Gonzalez-Hernandez, M. P., & Cruz-Albarran, I. A. (2024). Computer vision system based on the analysis of gait features for fall risk assessment in elderly people. Applied Sciences, 14(9),3867, https://doi.org/10.3390/app14093867
  • Chen, B., Chen, C., Hu, J., Sayeed, Z., Qi, J., Darwiche, H. F., Little, B. E., Lou, S., Darwish, M., Foote, C., & Palacio-Lascano, C. (2022). Computer vision and machine learning-based gait pattern recognition for flat fall prediction. Sensors, 22(20), 7960. https://doi.org/10.3390/s22207960
  • Cheung, J. C. W., Tam, E. W. C., Mak, A. H. Y., Chan, T. T. C., Lai, W. P. Y., & Zheng, Y. P. (2021). Night-time monitoring system (enightlog) for elderly wandering behavior. Sensors, 21(3), 704. https://doi.org/10.3390/s21030704
  • Fama, F., Faria, J. N. & Portugal, D. (2022). An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches. Internet of Things, 19, 100547, https://doi.org/10.1016/j.iot.2022.100547
  • Fernández-Bermejo, J., Martinez-del-Rincon, J., Dorado , J., del Toro , X., Santofimia, M. J., & Lopez, J. C. (2024). Edge computing transformers for fall detection in older adults. International Journal of Neural Systems, 34(5), 2450026. https://doi.org/10.1142/S0129065724500266
  • Ferreira, R. N., Ribeiro, N. F., & Santos, C. P. (2022). Fall risk assessment using wearable sensors: A narrative review. Sensors, 22(3), 984. https://doi.org/10.3390/s22030984
  • Fula, V., & Moreno, P. (2024). Wrist-based fall detection: Towards generalization across datasets. Sensors, 24(5), 1679. https://doi.org/10.3390/s24051679
  • Galvao, Y. M., Portela, L., Ferreira, J., Barros, P., Araujo Fagundes O. A., & Fernandes, B. J. T. (2021). A framework for anomaly identification applied on fall detection. IEEE Access, 9, 77264-77274. https://doi.org/10.1109/ACCESS.2021.3083064.
  • Garcia, E., Villar, M., Fanez, M., Villar, J. R., Cal, E., & Cho, S. B. (2022). Towards effective detection of elderly falls with CNN-LSTM neural networks. Neurocomputing, 500, 231-240, https://doi.org/10.1016/j.neucom.2021.06.102
  • Gharghan, S. K., & Hashim, H. A. (2024). A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques. Measurement, 226, 114186, https://doi.org/10.1016/j.measurement.2024.114186
  • Gonzalez-Canete, F. J., & Casilari, E. (2021). A feasibility study of the use of smartwatches in wearable fall detection systems. Sensors, 21(6), 2254. https://doi.org/10.3390/s21062254
  • Inertial Measurement Unit (IMU). Retrieved March 11, 2025 from https://en.m.wikipedia.org/wiki/Inertial_measurement_unit
  • Karar, M. E., Shehata, H. I., & Reyad, O. (2022). A survey of IoT-based fall detection for aiding elderly care: sensors, methods, challenges and future trends. Applied Sciences, 12(7), 3276. https://doi.org/10.3390/app12073276
  • Kim, T.H., Yuhai, O., Jeong, S., Kim, K., Kim, H., & Mun, J.H. (2022). Deep learning-based near-fall detection algorithm for fall risk monitoring system using a single inertial measurement unit. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2385-2394, https://doi.org/10.1109/TNSRE.2022.3199068
  • Kulurkar, P., Dixit, C. K., Bharathi, V. C., Monikavishnuvarthini, A., Dhakne, A., & Preethi, P. (2023), AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT. Measurement: Sensors, 25, 100614. https://doi.org/10.1016/j.measen.2022.100614
  • Lee, C. H., Mendoza, T., Huang, C. H., & Sun, T.H. (2025). Vision-based postural balance assessment of sit-to-stand transitions performed by younger and older adults. Gait & Posture, 117, 245-253. https://doi.org/10.1016/j.gaitpost.2025.01.001
  • Malche, T., Tharewal, S., Tiwari, P. K., Jabarulla, M. Y., Alnuaim, A. A., Hatamleh, W. A., Ullah M. A. (2022). Artificial Intelligence of Things- (AIoT-) based patient activity tracking system for remote patient monitoring. Journal of Healthcare Engineering, 8732213, https://doi.org/10.1155/2022/8732213
  • Mohan, D., Al-Hamid, D. Z., Chong, P.H.J., Sudheera, K .L. K., Gutierrez, J., Chan, H. C. B., & Li, H. (2024). Artificial intelligence and IoT in elderly fall prevention: a review. IEEE Sensors Journal, 24, 4181-4198, 15, https://doi:10.1109/JSEN.2023.3344605
  • Nahian, M. J. A., Ghosh, T., Banna, M. H. A., Aseeri, M. A., Uddin, M. N., Ahmed, M. R., Mahmud, M., & Kaiser, M.S. (2021). Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access, 9, 39413-39431. https://doi.org/10.1109/ACCESS.2021.3056441
  • Nooruddin, S., Islam, M. M., Sharna, F. A. Alhetari, H., & Kabir, M. N. (2022). Sensor-based fall detection systems: A review. J.Ambient Intell Human Comput, 13, 2735–2751. https://doi.org/10.1007/s12652-021-03248-z
  • Qian, Z., Lin, Y., Jing, W., Ma, Z., Liu, H., Yin, R., Li, Z., Bi, Z. & Zhang, W. (2022). Development of a real-time wearable fall detection system in the context of internet of things. IEEE internet of things journal, 9 (21), 21999-22007. https://doi.org/10.1109/JIOT.2022.3181701
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  • Ruiz-Ruiz, L., Jimenez, A. R., Garcia-Villamil, G., & Seco, F. (2021). Detecting fall risk and frailty in elders with inertial motion sensors: a survey of significant gait parameters. Sensors, 21(20), 6918. https://doi.org/10.3390/s21206918
  • Santiago, J., Cotto, E., Jaimes, L. G., & Vergara-Laurens, I. (2017). Fall detection system for the elderly. 2017 IEEE 7th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, 1-4. https://doi.org/10.1109/CCWC.2017.7868363
  • Seneviratne, S., Zoysa, J. D., Senarathna, S., Padmasiri, C., & Pallemulla, P. (2024). Robotic healthcare companion for the elderly and the differently abled with ındoor human following and fall detection capability. 2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC), Laguna Hills, CA, USA, 187-193. https://doi:10.1109/AIMHC59811.2024.00042
  • Silva, C. A., Casilari, E., & Bermudez, R. G. (2024). Cross-dataset evaluation of wearable fall detection systems using data from real falls and long-term monitoring of daily life. Measurement, 235, 114992, https://doi.org/10.1016/j.measurement.2024.114992
  • Sophini, S., Ilius, F. A. & Jamal, D. M., (2022). Wearable sensor systems for fall risk assessment: A review, Frontiers in Digital Health, 4, 921506. https://doi.org/10.3389/fdgth.2022.921506
  • Steenerson, K. K., Griswold, B., Keating, D. P., Srour, M., Burwinkel, J. R., Isanhart, E., Ma, Y., Fabry, D. A., Bhowmik, A. K., Jackler, R. K., & Fitzgerald, M. B. (2025). Use of hearing aids embedded with, inertial sensors and artificial intelligence to identify patients at risk for falling. Otol Neurotol, 46(2), 121-127. https://doi:10.1097/MAO.0000000000004386
  • Tang J., He, B., Xu, J., Tan, T., Wang, Z., Zhou, Y., & Jiang S. (2024). Synthetic IMU datasets and protocols can simplify fall detection experiments and optimize sensor configuration. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32, 1233-1245, https://doi.org/10.1109/TNSRE.2024.3370396
  • TCA9548A I2C Multiplexer. Retrieved March 11, 2025 from https://shop.pimoroni.com/products/tca9548a-i2c-multiplexer? variant =7461865921,
  • TCA9548A Low-Voltage 8-Channel I2-C Switch with Reset datasheet. Retrieved March 11, 2025 from https://www.ti.com/lit/ds/symlink/tca9548a.pdf
  • Xefteris, V.-R., Tsanousa, A. Meditskos, G., Vrochidis S., & Kompatsiaris, I. (2021). Performance, challenges, and limitations in multimodal fall detection systems: A review. IEEE sensors Journal, 21(17),18398-18409. https://doi.org/10.1109/JSEN.2021.3090454
  • Xiaoqun, Y. Jaehyuk, J., & Shuping, X. (2021). A large-scale open motion dataset (kfall) and benchmark algorithms for detecting pre-impact fall of the elderly using wearable inertial sensors. Frontiers in Aging Neuroscience, 13, https://doi.org/10.3389/fnagi.2021.692865
  • Xueyi, W., Joshua, E., & George, A., (2020). Elderly fall detection systems: A literature survey. Frontiers in Robotics and AI, 7. https://doi.org/10.3389/frobt.2020.00071
  • Vaiyapuri, T., Lydia, E. L., Sikkandar, M. Y., Diaz, V. G., Pustokhina, I.V., & Pustokhin, D. A. (2021). Internet of things and deep learning enabled elderly fall detection model for smart homecare. IEEE Access, 9, 113879-113888, https://doi.org/10.1109/ACCESS.2021.3094243
  • Villa, M., & Casilari, E. (2024). Wearable fall detectors based on low power transmission systems: a systematic review. Technologies, 12(9), 166. https://doi.org/10.3390/technologies12090166
  • Vimal, S., Robinson, Y. H., Kadry, S., Long, H. V., & Nam, Y., (2021). IoT based smart health monitoring with CNN using edge computing. Journal of Internet Technology, 22(1), 173-185, https://doi.org/10.3966/160792642021012201017
  • Yacchirema, D., Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and big data. Procedia Computer Science, 130, 603-610, https://doi.org/10.1016/j.procs.2018.04.110
  • Yu, S., Chai, Y., Chen, H., Brown, R. A., Sherman, S. J. & Nunamaker, J. F. (2021). Fall detection with wearable sensors: A hierarchical attention-based convolutional neural network approach. Journal of Management Information Systems, 38(4), 1095–1121. https://doi.org/10.1080/07421222.2021.1990617
  • Zhang, Q., Bao, X., Sun, S., & Lin, F. (2024). Lightweight network for small target fall detection based on feature fusion and dynamic convolution. J.Real-Time Image Proc, 21, 17. https://doi.org/10.1007/s11554-023-01397-2
  • Zurbuchen, N., Wilde, A., & Bruegger, P. (2021). A Machine learning multi-class approach for fall detection systems based on wearable sensors with a study on sampling rates selection. Sensors, 21(3), 938. https://doi.org/10.3390/s21030938
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Akış ve Sensör Verileri, Analog Elektronik ve Arayüzler/ Bağdaştıcılar, Gömülü Sistemler, Elektronik, Sensörler ve Dijital Donanım (Diğer), Tıbbi Robotik
Bölüm Araştırma Makaleleri
Yazarlar

Ahmet Turan 0000-0001-5653-9695

Duaa Warille 0009-0000-0434-3703

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 16 Mart 2025
Kabul Tarihi 4 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA Turan, A., & Warille, D. (2025). IMU Sensor Based Expandable Fall Detection System Design. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 15(1), 115-127. https://doi.org/10.54370/ordubtd.1658926