Monograf: Implementasi Komunikasi Nirkabel LoRa pada Desain Sepatu Diabetes Cerdas
Abstract
Diabetes adalah kondisi medis yang tersebar luas dan mempengaruhi sebagian besar populasi global. Penyakit ini adalah kelainan metabolisme yang dikenal sebagai diabetes melitus, yang ditandai dengan fluktuasi parah kadar glukosa darah karena produksi insulin yang tidak mencukupi dalam tubuh manusia.
Pemantauan diabetes yang efektif sangat penting bagi para peneliti karena berpotensi meningkatkan kualitas layanan kesehatan. Di antara tantangan yang dihadapi oleh penderita diabetes, salah satu masalah umum adalah berkembangnya ulkus kaki diabetes, yang sulit untuk dideteksi dengan cepat.
Kemajuan teknologi menawarkan jalan yang menjanjikan untuk pemantauan penyakit kronis seperti diabetes yang hemat biaya dan berkelanjutan. Buku ini menjelaskan tentang pengembangan desain sepatu diabetes cerdas berbasis IoT yang menggabungkan sensor tekanan dan pemantauan suhu untuk individu dengan permasalahan kaki diabetes
References
Adi, P. D. P., & Wahyu, Y. (2023). The error rate analyze and parameter measurement on LoRa communication for health monitoring. Microprocessors and Microsystems, 98, 104820. https://doi.org/https://doi.org/10.1016/j.micpro.2023.104820
Ahmed, S. A., Alwan, N. F., & Ali, A. M. (2022). Overview for Internet of Things: Basics, Components and Applications. Journal of University of Anbar for Pure Science, 12(3), 47–58. https://doi.org/10.37652/juaps.2022.171846
Alfandi, O. (2022). An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions. Mobile Networks and Applications, 27(3), 1299–1310. https://doi.org/10.1007/s11036-021-01892-5
Altayyar, S. S. (2021). Bare foot and in-shoe plantar pressure in diabetic males and females – is there difference? Medical Devices: Evidence and Research, 14, 271–276. https://doi.org/10.2147/MDER.S312739
Azit, N. A., Sahran, S., Leow, V. M., Subramaniam, M., Mokhtar, S., & Nawi, A. M. (2022). Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model. Heliyon, 8(10), e10772. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e10772
Belfiore, A., Cuccurullo, C., & Aria, M. (2022). IoT in healthcare: A scientometric analysis. Technological Forecasting and Social Change, 184, 122001. https://doi.org/https://doi.org/10.1016/j.techfore.2022.122001
Charles, R. K. J., Mary, A. B., Jenova, R., & Majid, M. A. (2019). VLSI design of intelligent, Self-monitored and managed, Strip-free, Non-invasive device for Diabetes mellitus patients to improve Glycemic control using IoT. Procedia Computer Science, 163, 117–124. https://doi.org/https://doi.org/10.1016/j.procs.2019.12.093
Chatwin, K. E., Abbott, C. A., Boulton, A. J. M., Bowling, F. L., & Reeves, N. D. (2020). The role of foot pressure measurement in the prediction and prevention of diabetic foot ulceration—A comprehensive review.
Diabetes/Metabolism Research and Reviews, 36(4), e3258. https://doi.org/https://doi.org/10.1002/dmrr.3258
Chen, M., Zhao, H., Shi, C., Chen, X., & Niu, D. (2023). Multi-scene LoRa positioning algorithm based on Kalman filter and its implementation on NS3. Ad Hoc Networks, 141, 103097. https://doi.org/https://doi.org/10.1016/j.adhoc.2023.103097
Coates, J., Chipperfield, A., & Clough, G. (2016). Wearable Multimodal Skin Sensing for the Diabetic Foot. In Electronics (Vol. 5, Issue 3). https://doi.org/10.3390/electronics5030045
de Araújo, A. L., Negreiros, F. D. da S., Florêncio, R. S., de Oliveira, S. K. P., da Silva, A. R. V., & Moreira, T. M. M. (2022). Effect of thermometry on the prevention of diabetic foot ulcers: a systematic review with meta-analysis*. Revista Latino-Americana de Enfermagem, 30. https://doi.org/10.1590/1518-8345.5663.3525
Eskofier, B. M., Lee, S. I., Baron, M., Simon, A., Martindale, C. F., Gaßner, H., & Klucken, J. (2017). An overview of smart shoes in the internet of health things: Gait and mobility assessment in health promotion and disease monitoring. Applied Sciences (Switzerland), 7(10). https://doi.org/10.3390/app7100986
Federation, I. D. (2011). IDF diabetes atlas. International Diabetes Federation, Executive Office.
Fernando, M. E., Crowther, R. G., & Wearing, S. (2018). The Importance of Foot Pressure in Diabetes BT - Handbook of Human Motion. In Handbook of Human Motion (pp. 759–787). Springer International Publishing. https://doi.org/10.1007/978-3-319-14418-4_39
Fierheller, M., & Sibbald, R. G. (2010). A Clinical Investigation into the Relationship between Increased Periwound Skin Temperature and Local Wound Infection in Patients with Chronic Leg Ulcers. Advances in Skin & Wound Care, 23(8). https://journals.lww.com/aswcjournal/Fulltext/2010/08000/A_Clinical_Investigation_into_the_Relationship.8.aspx
Firmansyah, D. W., Ichsan, M. H. H., & Bhawiyuga, A. (2020). Pengembangan Gateway LoRa-MQTT untuk Transmisi Data Dua Arah antara Wireless Sensor Network dan Cloud Server. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, 2548(1), 964X.
Ganesh, S., Saranya, S., Subaselvi, S. co., B, V., Kalyani, V., & P, I. (2022). Smart Footwear for Patients with Diabetic Neuropathy. Indian Journal of Natural Sciences, 13(71).
Ganie, S. M., & Malik, M. B. (2022). An ensemble Machine Learning approach for predicting Type-II diabetes mellitus based on lifestyle indicators. Healthcare Analytics, 2, 100092. https://doi.org/https://doi.org/10.1016/j.health.2022.100092
Gia, T. N., Ali, M., Dhaou, I. Ben, Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2017). IoT-based continuous glucose monitoring system: A feasibility study. Procedia Computer Science, 109, 327–334. https://doi.org/https://doi.org/10.1016/j.procs.2017.05.359
Haghi Kashani, M., Madanipour, M., Nikravan, M., Asghari, P., & Mahdipour, E. (2021). A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications, 192, 103164. https://doi.org/https://doi.org/10.1016/j.jnca.2021.103164
Hwang, A. (2019). Arduino Pressure Sensor Cushion for Tracking and Improving Sitting Posture. International Journal of Biomedical and Biological Engineering, 13(11), 454–460.
Jaiswal, A. K., Tiwari, P., Kumar, S., Al-Rakhami, M. S., Alrashoud, M., & Ghoneim, A. (2021). Deep Learning-Based Smart IoT Health System for Blindness Detection Using Retina Images. IEEE Access, 9, 70606–70615. https://doi.org/10.1109/ACCESS.2021.3078241
Jeffcoate, W. J., & Harding, K. G. (2003). Diabetic foot ulcers. The Lancet, 361(9368), 1545–1551. https://doi.org/10.1016/S0140-6736(03)13169-8
Jemima Jebaseeli, T., Anand Deva Durai, C., & Dinesh Peter, J. (2020). IOT based sustainable diabetic retinopathy diagnosis system. Sustainable Computing: Informatics and Systems, 28, 100272. https://doi.org/https://doi.org/10.1016/j.suscom.2018.08.004
Kularathne, N., Wijayathilaka, U., Kottawaththa, N., Hewakoralage, S., & Thelijjagoda, S. (2019). Dia-Shoe: A Smart Diabetic Shoe to Monitor and Prevent Diabetic Foot Ulcers. 2019 International Conference on Advancements in Computing (ICAC), 410–415. https://doi.org/10.1109/ICAC49085.2019.9103408
Kulkarni, V. V., & Seraj, F. (2020). An embedded wearable device for monitoring diabetic foot ulcer parameters. ACM International Conference Proceeding Series, 345–351. https://doi.org/10.1145/3389189.3397982
Lavery, L. A., Higgins, K. R., Lanctot, D. R., Constantinides, G. P., Zamorano, R. G., Armstrong, D. G., Athanasiou, K. A., & Agrawal, C. M. (2004). Home Monitoring of Foot Skin Temperatures to Prevent Ulceration. Diabetes Care, 27(11), 2642–2647. https://doi.org/10.2337/diacare.27.11.2642
Monteiro-Soares, M., Russell, D., Boyko, E. J., Jeffcoate, W., Mills, J. L., Morbach, S., & Game, F. (2019). IWGDF Guideline on the classification of diabetic foot ulcers. International Working Group on the Diabetic Foot, 1–15.
Nguyen Gia, T., Dhaou, I. Ben, Ali, M., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2019). Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Computer Systems, 93, 198–211. https://doi.org/https://doi.org/10.1016/j.future.2018.10.029
Nicolucci, A., Romeo, L., Bernardini, M., Vespasiani, M., Rossi, M. C., Petrelli, M., Ceriello, A., Di Bartolo, P., Frontoni, E., & Vespasiani, G. (2022). Prediction of complications of type 2 Diabetes: A Machine learning approach. Diabetes Research and Clinical Practice, 190, 110013. https://doi.org/https://doi.org/10.1016/j.diabres.2022.110013
Palaniswamy, T., & Vellingiri, M. (2023). Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images. IEEE Access, 11, 27590–27601. https://doi.org/10.1109/ACCESS.2023.3257988
Perez, A. J., Siddiqui, F., Zeadally, S., & Lane, D. (2023). A review of IoT systems to enable independence for the elderly and disabled individuals. Internet of Things, 21, 100653. https://doi.org/https://doi.org/10.1016/j.iot.2022.100653
Permana, H., Koesoemadinata, R. C., Soetedjo, N. N. M., Dewi, N. F., Jayanti, N., Imaculata, S., Ruslami, R., Alisjahbana, B., & McAllister, S. M. (2022). Diabetes mellitus patients in Indonesia: management in a tertiary hospital compared to primary health care. Universa Medicina, 41(2), 157–168. https://doi.org/10.18051/UnivMed.2022.v41.157-168
Prastyo, E. A. (2023). Prinsip Kerja dari LoRa SX1278. https://www.arduinoindonesia.id/2022/08/prinsip-kerja-dari-lora-sx1278.html
Rahimi khoygani, E., Rahimi khoygani, M. R., & Ghasemi, R. (2021). Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method. International Journal of Robotics and Control Systems, 1(3), 338–354. https://doi.org/10.31763/ijrcs.v1i3.442
Rayan, R., Τsagkaris, C., & Romash, I. (2021). The Internet of Things for Healthcare: Applications, Selected Cases and Challenges. In IoT in Healthcare and Ambient Assisted Living (pp. 1–15). https://doi.org/10.1007/978-981-15-9897-5_1
Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., & Iranmanesh, M. (2023). The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things, 22, 100721. https://doi.org/https://doi.org/10.1016/j.iot.2023.100721
Rezaee, N., Zanjirchi, S. M., Jalilian, N., & Bamakan, S. M. H. (2023). Internet of things empowering operations management; A systematic review based on bibliometric and content analysis. Telematics and Informatics Reports, 11, 100096. https://doi.org/https://doi.org/10.1016/j.teler.2023.100096
Schiel, R., Bambauer, R., & Steveling, A. (2018). Technology in Diabetes Treatment: Update and Future. Artificial Organs, 42(11), 1017–1027. https://doi.org/10.1111/aor.13296
Soliman, S., Oudah, W., & Aljuhani, A. (2023). Deep learning-based intrusion detection approach for securing industrial Internet of Things. Alexandria Engineering Journal, 81, 371–383. https://doi.org/https://doi.org/10.1016/j.aej.2023.09.023
Sutkowska, E., Sutkowski, K., Sokołowski, M., Franek, E., & Dragan, S. (2019). Distribution of the Highest Plantar Pressure Regions in Patients with Diabetes and Its Association with Peripheral Neuropathy, Gender, Age, and BMI: One Centre Study. Journal of Diabetes Research, 2019, 7395769. https://doi.org/10.1155/2019/7395769
Verma, H., Chauhan, N., & Awasthi, L. K. (2023). A Comprehensive review of ‘Internet of Healthcare Things’: Networking aspects, technologies, services, applications, challenges, and security concerns. Computer Science Review, 50, 100591. https://doi.org/https://doi.org/10.1016/j.cosrev.2023.100591
Wibowo, D. B. (2018). Pengukuran Distribusi Beban Telapak Kaki Manusia Saat Berdiri Tegak Menggunakan Sensor FSR 402. Rotasi, 20(1), 22. https://doi.org/10.14710/rotasi.20.1.22-28
Zhang, X., Wang, H., Du, C., Fan, X., Cui, L., Chen, H., Deng, F., Tong, Q., He, M., Yang, M., Tan, X., Li, L., Liang, Z., Chen, Y., Chen, D., Armstrong, D. G., & Deng, W. (2022). Custom-Molded Offloading Footwear Effectively Prevents Recurrence and Amputation, and Lowers Mortality Rates in High-Risk Diabetic Foot Patients: A Multicenter, Prospective Observational Study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 15(December 2021), 103–109. https://doi.org/10.2147/DMSO.S341364
Ahmed, S. A., Alwan, N. F., & Ali, A. M. (2022). Overview for Internet of Things: Basics, Components and Applications. Journal of University of Anbar for Pure Science, 12(3), 47–58. https://doi.org/10.37652/juaps.2022.171846
Alfandi, O. (2022). An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions. Mobile Networks and Applications, 27(3), 1299–1310. https://doi.org/10.1007/s11036-021-01892-5
Altayyar, S. S. (2021). Bare foot and in-shoe plantar pressure in diabetic males and females – is there difference? Medical Devices: Evidence and Research, 14, 271–276. https://doi.org/10.2147/MDER.S312739
Azit, N. A., Sahran, S., Leow, V. M., Subramaniam, M., Mokhtar, S., & Nawi, A. M. (2022). Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model. Heliyon, 8(10), e10772. https://doi.org/https://doi.org/10.1016/j.heliyon.2022.e10772
Belfiore, A., Cuccurullo, C., & Aria, M. (2022). IoT in healthcare: A scientometric analysis. Technological Forecasting and Social Change, 184, 122001. https://doi.org/https://doi.org/10.1016/j.techfore.2022.122001
Charles, R. K. J., Mary, A. B., Jenova, R., & Majid, M. A. (2019). VLSI design of intelligent, Self-monitored and managed, Strip-free, Non-invasive device for Diabetes mellitus patients to improve Glycemic control using IoT. Procedia Computer Science, 163, 117–124. https://doi.org/https://doi.org/10.1016/j.procs.2019.12.093
Chatwin, K. E., Abbott, C. A., Boulton, A. J. M., Bowling, F. L., & Reeves, N. D. (2020). The role of foot pressure measurement in the prediction and prevention of diabetic foot ulceration—A comprehensive review.
Diabetes/Metabolism Research and Reviews, 36(4), e3258. https://doi.org/https://doi.org/10.1002/dmrr.3258
Chen, M., Zhao, H., Shi, C., Chen, X., & Niu, D. (2023). Multi-scene LoRa positioning algorithm based on Kalman filter and its implementation on NS3. Ad Hoc Networks, 141, 103097. https://doi.org/https://doi.org/10.1016/j.adhoc.2023.103097
Coates, J., Chipperfield, A., & Clough, G. (2016). Wearable Multimodal Skin Sensing for the Diabetic Foot. In Electronics (Vol. 5, Issue 3). https://doi.org/10.3390/electronics5030045
de Araújo, A. L., Negreiros, F. D. da S., Florêncio, R. S., de Oliveira, S. K. P., da Silva, A. R. V., & Moreira, T. M. M. (2022). Effect of thermometry on the prevention of diabetic foot ulcers: a systematic review with meta-analysis*. Revista Latino-Americana de Enfermagem, 30. https://doi.org/10.1590/1518-8345.5663.3525
Eskofier, B. M., Lee, S. I., Baron, M., Simon, A., Martindale, C. F., Gaßner, H., & Klucken, J. (2017). An overview of smart shoes in the internet of health things: Gait and mobility assessment in health promotion and disease monitoring. Applied Sciences (Switzerland), 7(10). https://doi.org/10.3390/app7100986
Federation, I. D. (2011). IDF diabetes atlas. International Diabetes Federation, Executive Office.
Fernando, M. E., Crowther, R. G., & Wearing, S. (2018). The Importance of Foot Pressure in Diabetes BT - Handbook of Human Motion. In Handbook of Human Motion (pp. 759–787). Springer International Publishing. https://doi.org/10.1007/978-3-319-14418-4_39
Fierheller, M., & Sibbald, R. G. (2010). A Clinical Investigation into the Relationship between Increased Periwound Skin Temperature and Local Wound Infection in Patients with Chronic Leg Ulcers. Advances in Skin & Wound Care, 23(8). https://journals.lww.com/aswcjournal/Fulltext/2010/08000/A_Clinical_Investigation_into_the_Relationship.8.aspx
Firmansyah, D. W., Ichsan, M. H. H., & Bhawiyuga, A. (2020). Pengembangan Gateway LoRa-MQTT untuk Transmisi Data Dua Arah antara Wireless Sensor Network dan Cloud Server. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, 2548(1), 964X.
Ganesh, S., Saranya, S., Subaselvi, S. co., B, V., Kalyani, V., & P, I. (2022). Smart Footwear for Patients with Diabetic Neuropathy. Indian Journal of Natural Sciences, 13(71).
Ganie, S. M., & Malik, M. B. (2022). An ensemble Machine Learning approach for predicting Type-II diabetes mellitus based on lifestyle indicators. Healthcare Analytics, 2, 100092. https://doi.org/https://doi.org/10.1016/j.health.2022.100092
Gia, T. N., Ali, M., Dhaou, I. Ben, Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2017). IoT-based continuous glucose monitoring system: A feasibility study. Procedia Computer Science, 109, 327–334. https://doi.org/https://doi.org/10.1016/j.procs.2017.05.359
Haghi Kashani, M., Madanipour, M., Nikravan, M., Asghari, P., & Mahdipour, E. (2021). A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications, 192, 103164. https://doi.org/https://doi.org/10.1016/j.jnca.2021.103164
Hwang, A. (2019). Arduino Pressure Sensor Cushion for Tracking and Improving Sitting Posture. International Journal of Biomedical and Biological Engineering, 13(11), 454–460.
Jaiswal, A. K., Tiwari, P., Kumar, S., Al-Rakhami, M. S., Alrashoud, M., & Ghoneim, A. (2021). Deep Learning-Based Smart IoT Health System for Blindness Detection Using Retina Images. IEEE Access, 9, 70606–70615. https://doi.org/10.1109/ACCESS.2021.3078241
Jeffcoate, W. J., & Harding, K. G. (2003). Diabetic foot ulcers. The Lancet, 361(9368), 1545–1551. https://doi.org/10.1016/S0140-6736(03)13169-8
Jemima Jebaseeli, T., Anand Deva Durai, C., & Dinesh Peter, J. (2020). IOT based sustainable diabetic retinopathy diagnosis system. Sustainable Computing: Informatics and Systems, 28, 100272. https://doi.org/https://doi.org/10.1016/j.suscom.2018.08.004
Kularathne, N., Wijayathilaka, U., Kottawaththa, N., Hewakoralage, S., & Thelijjagoda, S. (2019). Dia-Shoe: A Smart Diabetic Shoe to Monitor and Prevent Diabetic Foot Ulcers. 2019 International Conference on Advancements in Computing (ICAC), 410–415. https://doi.org/10.1109/ICAC49085.2019.9103408
Kulkarni, V. V., & Seraj, F. (2020). An embedded wearable device for monitoring diabetic foot ulcer parameters. ACM International Conference Proceeding Series, 345–351. https://doi.org/10.1145/3389189.3397982
Lavery, L. A., Higgins, K. R., Lanctot, D. R., Constantinides, G. P., Zamorano, R. G., Armstrong, D. G., Athanasiou, K. A., & Agrawal, C. M. (2004). Home Monitoring of Foot Skin Temperatures to Prevent Ulceration. Diabetes Care, 27(11), 2642–2647. https://doi.org/10.2337/diacare.27.11.2642
Monteiro-Soares, M., Russell, D., Boyko, E. J., Jeffcoate, W., Mills, J. L., Morbach, S., & Game, F. (2019). IWGDF Guideline on the classification of diabetic foot ulcers. International Working Group on the Diabetic Foot, 1–15.
Nguyen Gia, T., Dhaou, I. Ben, Ali, M., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2019). Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Computer Systems, 93, 198–211. https://doi.org/https://doi.org/10.1016/j.future.2018.10.029
Nicolucci, A., Romeo, L., Bernardini, M., Vespasiani, M., Rossi, M. C., Petrelli, M., Ceriello, A., Di Bartolo, P., Frontoni, E., & Vespasiani, G. (2022). Prediction of complications of type 2 Diabetes: A Machine learning approach. Diabetes Research and Clinical Practice, 190, 110013. https://doi.org/https://doi.org/10.1016/j.diabres.2022.110013
Palaniswamy, T., & Vellingiri, M. (2023). Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images. IEEE Access, 11, 27590–27601. https://doi.org/10.1109/ACCESS.2023.3257988
Perez, A. J., Siddiqui, F., Zeadally, S., & Lane, D. (2023). A review of IoT systems to enable independence for the elderly and disabled individuals. Internet of Things, 21, 100653. https://doi.org/https://doi.org/10.1016/j.iot.2022.100653
Permana, H., Koesoemadinata, R. C., Soetedjo, N. N. M., Dewi, N. F., Jayanti, N., Imaculata, S., Ruslami, R., Alisjahbana, B., & McAllister, S. M. (2022). Diabetes mellitus patients in Indonesia: management in a tertiary hospital compared to primary health care. Universa Medicina, 41(2), 157–168. https://doi.org/10.18051/UnivMed.2022.v41.157-168
Prastyo, E. A. (2023). Prinsip Kerja dari LoRa SX1278. https://www.arduinoindonesia.id/2022/08/prinsip-kerja-dari-lora-sx1278.html
Rahimi khoygani, E., Rahimi khoygani, M. R., & Ghasemi, R. (2021). Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method. International Journal of Robotics and Control Systems, 1(3), 338–354. https://doi.org/10.31763/ijrcs.v1i3.442
Rayan, R., Τsagkaris, C., & Romash, I. (2021). The Internet of Things for Healthcare: Applications, Selected Cases and Challenges. In IoT in Healthcare and Ambient Assisted Living (pp. 1–15). https://doi.org/10.1007/978-981-15-9897-5_1
Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., & Iranmanesh, M. (2023). The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things, 22, 100721. https://doi.org/https://doi.org/10.1016/j.iot.2023.100721
Rezaee, N., Zanjirchi, S. M., Jalilian, N., & Bamakan, S. M. H. (2023). Internet of things empowering operations management; A systematic review based on bibliometric and content analysis. Telematics and Informatics Reports, 11, 100096. https://doi.org/https://doi.org/10.1016/j.teler.2023.100096
Schiel, R., Bambauer, R., & Steveling, A. (2018). Technology in Diabetes Treatment: Update and Future. Artificial Organs, 42(11), 1017–1027. https://doi.org/10.1111/aor.13296
Soliman, S., Oudah, W., & Aljuhani, A. (2023). Deep learning-based intrusion detection approach for securing industrial Internet of Things. Alexandria Engineering Journal, 81, 371–383. https://doi.org/https://doi.org/10.1016/j.aej.2023.09.023
Sutkowska, E., Sutkowski, K., Sokołowski, M., Franek, E., & Dragan, S. (2019). Distribution of the Highest Plantar Pressure Regions in Patients with Diabetes and Its Association with Peripheral Neuropathy, Gender, Age, and BMI: One Centre Study. Journal of Diabetes Research, 2019, 7395769. https://doi.org/10.1155/2019/7395769
Verma, H., Chauhan, N., & Awasthi, L. K. (2023). A Comprehensive review of ‘Internet of Healthcare Things’: Networking aspects, technologies, services, applications, challenges, and security concerns. Computer Science Review, 50, 100591. https://doi.org/https://doi.org/10.1016/j.cosrev.2023.100591
Wibowo, D. B. (2018). Pengukuran Distribusi Beban Telapak Kaki Manusia Saat Berdiri Tegak Menggunakan Sensor FSR 402. Rotasi, 20(1), 22. https://doi.org/10.14710/rotasi.20.1.22-28
Zhang, X., Wang, H., Du, C., Fan, X., Cui, L., Chen, H., Deng, F., Tong, Q., He, M., Yang, M., Tan, X., Li, L., Liang, Z., Chen, Y., Chen, D., Armstrong, D. G., & Deng, W. (2022). Custom-Molded Offloading Footwear Effectively Prevents Recurrence and Amputation, and Lowers Mortality Rates in High-Risk Diabetic Foot Patients: A Multicenter, Prospective Observational Study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 15(December 2021), 103–109. https://doi.org/10.2147/DMSO.S341364
