Intelligent alarm system for people with physical disability: design and a pilot study from Iran

Main Article Content

Roshanak Tirdad
Piruz Nami
Shayan Samieyan
Fakher Rahim

Abstract

At present, there are examples that the patient's heart rate is measured by placing a finger on a device or it is possible with the help of mobile phones. But all of these tools are effective only when the patient becomes aware of the fluctuations.  In this paper, we attempt to design a system to prevent possible dangers faced by patients with various types of disabilities, which, in the event of a place of an attack, reveals the physical condition and vital signs. The system consists of two parts, including software and hardware parts. The hardware part includes sensors and central controllers. While the software part includes an installable application on the user's mobile phone. This app is composed of two parts, including patient-side or transmitter application (Sender), and relatives, hospital, emergency service provider or doctor-side or receiver application (Receiver). We recruited 15 patients with physical disabilities from whom data were acquired. Data of 22 falling events were collected; altogether, 19 falling events were submitted for analysis. The rest of the events were rejected because of age restriction inclusion criteria. Findings have confirmed the helpfulness and usefulness of the method to process the proposed model properly and detect, track, and classify physically disabled people as moving objects. Our findings demonstrated the rational performance of the suggested fall detection system in the tested situations.

Article Details

How to Cite
1.
Tirdad R, Nami P, Samieyan S, Rahim F. Intelligent alarm system for people with physical disability: design and a pilot study from Iran. J Public Hlth Dev [Internet]. 2021 May 19 [cited 2024 Apr. 27];19(2):47-63. Available from: https://he01.tci-thaijo.org/index.php/AIHD-MU/article/view/246145
Section
Original Articles
Author Biographies

Roshanak Tirdad, Neurology Department, Golestan Hospital, Jundishapur Medical University, Iran

 

 

Piruz Nami , University of Science and Research, Azad University, Ahvaz, Iran

 

 

Shayan Samieyan, University of Science and Research, Azad University, Ahvaz, Iran

 

 

References

Ortiz E, Clancy CM: Use of Information Technology to Improve the Quality of Health Care in the United States. Health Services Research 2003, 38(2):xi-xxii.

Kakria P, Tripathi NK, Kitipawang P: A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors. International Journal of Telemedicine and Applications 2015, 2015:373474.

Morris CJ, Hastings JA, Boyd K, Krainski F, Perhonen MA, Scheer FAJL, Levine BD: Day/Night Variability in Blood Pressure: Influence of Posture and Physical Activity. American Journal of Hypertension 2013, 26(6):822-828.

Patel S, Park H, Bonato P, Chan L, Rodgers M: A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation 2012, 9:21-21.

Stalenhoef PA, Diederiks JP, Knottnerus JA, Kester AD, Crebolder HF: A risk model for the prediction of recurrent falls in community-dwelling elderly: a prospective cohort study. Journal of clinical epidemiology 2002, 55(11):1088-1094.

Hung CH, Wang CJ, Tang TC, Chen LY, Peng LN, Hsiao FY, Chen LK: Recurrent falls and its risk factors among older men living in the veterans retirement communities: A cross-sectional study. Archives of gerontology and geriatrics 2017, 70:214-218.

Talarska D, Strugala M, Szewczyczak M, Tobis S, Michalak M, Wroblewska I, Wieczorowska-Tobis K: Is independence of older adults safe considering the risk of falls? BMC geriatrics 2017, 17(1):66.

Lancioni GE, Van Bergen I, Furniss F: Urine alarms and prompts for fostering daytime urinary continence in a student with multiple disabilities: a replication study. Perceptual and motor skills 2002, 94(3 Pt 1):867-870.

Arends JB, van Dorp J, van Hoek D, Kramer N, van Mierlo P, van der Vorst D, Tan FI: Diagnostic accuracy of audio-based seizure detection in patients with severe epilepsy and an intellectual disability. Epilepsy & behavior : E&B 2016, 62:180-185.

Frontoni E, Pollini R, Russo P, Zingaretti P, Cerri G: HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly. Sensors (Basel, Switzerland) 2017, 17(11).

García-Peñalvo FJ, Franco-Martín M: Sensor Technologies for Caring People with Disabilities. Sensors (Basel, Switzerland) 2019, 19(22).

de Miguel K, Brunete A, Hernando M, Gambao E: Home Camera-Based Fall Detection System for the Elderly. Sensors (Basel, Switzerland) 2017, 17(12).

Ejupi A, Galang C, Aziz O, Park EJ, Robinovitch S: Accuracy of a wavelet-based fall detection approach using an accelerometer and a barometric pressure sensor. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2017, 2017:2150-2153.

Mao A, Ma X, He Y, Luo J: Highly Portable, Sensor-Based System for Human Fall Monitoring. Sensors (Basel, Switzerland) 2017, 17(9).

Lin T-H, Yang C-Y, Shih W-P: Fall Prevention Shoes Using Camera-Based Line-Laser Obstacle Detection System. Journal of Healthcare Engineering 2017, 2017:11.

Tran T-H, Le T-L, Hoang V-N, Vu H: Continuous detection of human fall using multimodal features from Kinect sensors in scalable environment. Computer Methods and Programs in Biomedicine 2017, 146(Supplement C):151-165.

Ahmed M, Mehmood N, Nadeem A, Mehmood A, Rizwan K: Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data. Healthcare informatics research 2017, 23(3):147-158.

Aziz O, Klenk J, Schwickert L, Chiari L, Becker C, Park EJ, Mori G, Robinovitch SN: Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets. PLoS ONE 2017, 12(7):e0180318.

Hsieh CY, Liu KC, Huang CN, Chu WC, Chan CT: Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model. Sensors (Basel, Switzerland) 2017, 17(2).

Tsinganos P, Skodras A: On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection. Sensors (Basel, Switzerland) 2018, 18(2).

Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA: A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application. International journal of medical informatics 2018, 112:173-184.

Santoyo-Ramón JA, Casilari E, Cano-García JM: Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning. Sensors (Basel, Switzerland) 2018, 18(4).

Cates B, Sim T, Heo HM, Kim B, Kim H, Mun JH: A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System. Sensors (Basel, Switzerland) 2018, 18(4).

Khojasteh SB, Villar JR, Chira C, González VM, de la Cal E: Improving Fall Detection Using an On-Wrist Wearable Accelerometer. Sensors (Basel, Switzerland) 2018, 18(5).

Coahran M, Hillier LM, Van Bussel L, Black E, Churchyard R, Gutmanis I, Ioannou Y, Michael K, Ross T, Mihailidis A: Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses' Perceptions and Lessons Learned. Canadian journal on aging = La revue canadienne du vieillissement 2018, 37(3):245-260.

Mauldin TR, Canby ME, Metsis V, Ngu AHH, Rivera CC: SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors (Basel, Switzerland) 2018, 18(10).

Lukas CJ, Yahya FB, Breiholz J, Roy A, Chen X, Patel HN, Liu N, Kosari A, Li S, Akella Kamakshi D et al: A 1.02 μW Battery-Less, Continuous Sensing and Post-Processing SiP for Wearable Applications. IEEE transactions on biomedical circuits and systems 2019, 13(2):271-281.

Santos GL, Endo PT, Monteiro KHC, Rocha EDS, Silva I, Lynn T: Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors (Basel, Switzerland) 2019, 19(7).

Saadeh W, Butt SA, Altaf MAB: A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2019, 27(5):995-1003.

Miao S, Chen G, Ning X, Zi Y, Ren K, Bing Z, Knoll A: Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection. Frontiers in neurorobotics 2019, 13:38.

Kong X, Chen L, Wang Z, Chen Y, Meng L, Tomiyama H: Robust Self-Adaptation Fall-Detection System Based on Camera Height. Sensors (Basel, Switzerland) 2019, 19(17).

Kamel Gharghan S, Saad Fakhrulddin S, Al-Naji A, Chahl J: Energy-Efficient Elderly Fall Detection System Based on Power Reduction and Wireless Power Transfer. Sensors (Basel, Switzerland) 2019, 19(20).

Khan S, Qamar R, Zaheen R, Al-Ali AR, Al Nabulsi A, Al-Nashash H: Internet of things based multi-sensor patient fall detection system. Healthcare technology letters 2019, 6(5):132-137.

Tahir A, Morison G, Skelton DA, Gibson RM: Hardware/Software Co-design of Fractal Features based Fall Detection System. Sensors (Basel, Switzerland) 2020, 20(8).

Chander H, Burch RF, Talegaonkar P, Saucier D, Luczak T, Ball JE, Turner A, Kodithuwakku Arachchige SNK, Carroll W, Smith BK et al: Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. International journal of environmental research and public health 2020, 17(10).