FallDS-IoT: A Fall Detection System for Elderly Healthcare Based on IoT Data Analytics

Bhoi, Sourav Kumar; Panda, Sanjaya Kumar; Patra, Bivash; Pradhan, Bijaya; Priyadarshinee, Priyanka; Tripathy, Swaroop; Mallick, Chittaranjan; Singh, Munesh; Khilar, Pabitra Mohan

doi:10.1109/icit.2018.00041

Cited by 27 publications

(5 citation statements)

References 24 publications

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“…The Fall Detection device based on Internet of Things (FallDS-IoT) created a wearable gadget that uses accelerometer and gyroscope sensors to detect falls in older people [13]. A device that employs a passive RFID sensor tag with the RFMicron Magnus S chip and can track pressure in addition to RSSI was suggested by another study.…”

Section: Review Of Literaturementioning

confidence: 99%

نظام مدمج منخفض التكلفة للكشف عن السقوط وتنبيه الطوارئ باستخدام إنترنت الأشياء لكبار السن

صديق,

صابر,

أنجوم

et al. 2024

benkj

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كثيرًا ما يواجه كبار السن مشكلات كبيرة تتعلق بالسقوط، مما يؤدي إلى حدوث إصابات وفقدان الحرية. في هذا البحث، نقترح نظام إنترنت الأشياء المدمج وغير المكلف لاكتشاف حالات السقوط لكبار السن وتنبيهات الطوارئ. ووفقا لمنظمة الصحة العالمية، فإن السقوط هو السبب العالمي الثاني الأكثر شيوعا للوفيات الناجمة عن الإصابات غير المقصودة أو العرضية، وهو ما يمثل ما يقدر بنحو 646 ألف حالة وفاة سنويا. بالإضافة إلى الإصابات القاتلة، يمكن أن يؤدي السقوط إلى إصابات غير مميتة بما في ذلك كسور الورك، والتي يمكن أن تقلل بشكل كبير من القدرة على الحركة والحرية. يتكون النظام من جهاز سهل اللصق وقابل للفصل ويمكن ارتداؤه على الحزام أو في جيب القميص. مكونات هذه الأداة: مودم SIM800L GSM، ومستشعر جيروسكوبي MPU9250 ذو 9 محاور، ووحدة GPS، وزر الذعر، وبطارية قابلة لإعادة الشحن. يتم اكتشاف السقوط بواسطة مستشعر MPU9250، ويمكن إرسال تحذير للطوارئ عن طريق الضغط على زر الذعر. حيث تم تصميم النظام ليكون غير مكلف، وسهل التشغيل، ويمكن الاعتماد عليه، مما يجعله مناسبًا للتطبيق على نطاق واسع في رعاية المسنين. ووفقا للنتائج الأولية، يمكن للنظام تحديد السقوط بشكل موثوق وإصدار إنذارات الطوارئ، مما يثبت قدرته على زيادة سلامة ورفاهية كبار السن.

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Section: Review Of Literaturementioning

confidence: 99%

نظام مدمج منخفض التكلفة للكشف عن السقوط وتنبيه الطوارئ باستخدام إنترنت الأشياء لكبار السن

صديق,

صابر,

أنجوم

et al. 2024

benkj

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“…Image classification can be effected to determine fall status with the help of the linear SVM. In [17], the authors address this issue by presenting an FD System related to IoT (FallDS-IoT) by framing wearable systems for FD of older adults. The authors utilize Gyroscope sensors and an Accelerometer to get precise outcomes of FD.…”

Section: Literature Reviewmentioning

confidence: 99%

Teamwork Optimization with Deep Learning Based Fall Detection for營oT-Enabled Smart Healthcare System

Basahel¹,

Bajaba²,

Yamin³

et al. 2023

Computers, Materials &Amp; Continua

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The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT, AI, smartphones, wearables, and so on making it possible to design fall detection (FD) systems for smart home care. This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems (TWODL-FDSHS). The TWODL-FDSHS technique's goal is to detect fall events for a smart healthcare system. Initially, the presented TWODL-FDSHS technique exploits IoT devices for the data collection process. Next, the TWODL-FDSHS technique applies the TWO with Capsule Network (CapsNet) model for feature extraction. At last, a deep random vector functional link network (DRVFLN) with an Adam optimizer is exploited for fall event detection. A wide range of simulations took place to exhibit the enhanced performance of the presented TWODL-FDSHS technique. The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30% on the URFD dataset.

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“…Applications demand input from users or other smart devices capable of bidirectional communication. The processing time needed by a computational intelligence (CI) algorithm that processes input is also greater than is available on constrained hardware [8]. The aged at home are susceptible to falling due to a variety of issues, including heart attacks, physical impairments, low blood pressure, etc.…”

Section: Data Analysis In Iotmentioning

confidence: 99%

Medical Data Analytics and Wearable Devices

Manoharan

J²,

Sowmiya

et al. 2022

EAI Endorsed Trans Smart Cities

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Clinical decision-making may be directly impacted by wearable application. Some people think that wearable technologies, such as patient rehabilitation outside of hospitals, could boost patient care quality while lowering costs. The big data produced by wearable technology presents researchers with both a challenge and an opportunity to expand the use of artificial intelligence (AI) techniques on these data. By establishing new healthcare service systems, it is possible to organise diverse information and communications technologies into service linkages. This includes emerging smart systems, cloud computing, social networks, and enhanced sensing and data analysis techniques. The characteristics and features of big data, the significance of big data analytics in the healthcare industry, and a discussion of the effectiveness of several machine learning algorithms employed in big data analytics served as our conclusion.

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FallDS-IoT: A Fall Detection System for Elderly Healthcare Based on IoT Data Analytics

Cited by 27 publications

References 24 publications

نظام مدمج منخفض التكلفة للكشف عن السقوط وتنبيه الطوارئ باستخدام إنترنت الأشياء لكبار السن

نظام مدمج منخفض التكلفة للكشف عن السقوط وتنبيه الطوارئ باستخدام إنترنت الأشياء لكبار السن

Teamwork Optimization with Deep Learning Based Fall Detection for營oT-Enabled Smart Healthcare System

Medical Data Analytics and Wearable Devices

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