Practical fall detection based on IoT technologies: A survey

Mozaffari, Nassim; Rezazadeh, Javad; Farahbakhsh, Reza; Yazdani, Saeed; Sandrasegaran, Kumbesan

doi:10.1016/j.iot.2019.100124

Cited by 45 publications

(19 citation statements)

References 95 publications

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“…In contrast, Mozaffari et al [40] took into account a more human aspect of IoT devices. They considered the three stages of falling people (prediction, prevention and detection) to develop a diagnostic system for falls in smart buildings.…”

Section: Related Workmentioning

confidence: 99%

“…None considers the analysis or discovery of the residents' behavior patterns to improve the automatic control of domotic devices. Most papers expose issues related to energy-saving [8,9,13,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], security [36][37][38][39], data analysis [10], home system design [14,15] and healthcare [40][41][42]. After the reviewing these papers, a lack of smart configuration schemes that balance energy saving and comfort by analyzing user interaction with home automation devices was identified.…”

Section: Related Workmentioning

confidence: 99%

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Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

et al. 2021

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In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

et al. 2021

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“…Also, RHM aims to enable an uninterrupted and real time observation for patients and elderly people in order to diagnose, manage, and prevent disease [46,47]. That includes: a) Diabetic patients [41] b) Heart diseases [48] c) Cardiovascular diseases [49] d) Blood diseases [50] e) Mental diseases [51] f) Arthritis disease [52] g) Fall detection and prediction [53] h) Activity detection and recognition [54] i)…”

Section: Related Workmentioning

confidence: 99%

A review of remote health monitoring based on internet of things

AlShorman

Alshorman²,

Masadeh³

et al. 2021

IJEECS

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<span>Managing, diagnosis, prognosis, continuous monitoring, early detection, and preventing chronic diseases for patients and elderly people have been gained a crucial role nowadays. However, elderly people with chronic health conditions such as diabetes, cardiovascular disease, and mental diseases, need special health care. With the help of the internet of things (IoT) technologies, remote health monitoring (RHM) helps patients, caregivers, and countries for improving healthcare services, such as medical files services, mobile healthcare (mhealth), telemedicine services, and sensing technology. Moreover, RHM aims to reduce hospitalized demands and costs. The main contribution of the proposed study is to review RHM studies based on IoT technologies. Moreover, the challenges and possible future trends of RMH are highlighted.</span>

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“…Miniaturized sensors attached or worn on the user's body, along with ambient sensors, are a new generation of computers that can be tailored to tackle healthcare management challenges. Applications that have been developed include chronic and non-communicable disease monitoring [1][2][3][4][5][6][7], fall monitoring [8][9][10], emergency care management [11][12][13], rehabilitation [14][15][16][17], fitness and lifestyle tracking [18,19], and sport performance monitoring [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Light-Weight Symmetric Encryption Algorithm for Secure Data Transmission via BLE Beacons

Banani

Thiemjarus

Wongthavarawat

et al. 2021

JSAN

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Pervasive sensing with Body Sensor Networks (BSNs) is a promising technology for continuous health monitoring. Since the sensor nodes are resource-limited, on-node processing and advertisement of digested information via BLE beacon is a promising technique that can enable a node gateway to communicate with more sensor nodes and extend the sensor node’s lifetime before requiring recharging. This study proposes a Dynamic Light-weight Symmetric (DLS) encryption algorithm designed and developed to address the challenges in data protection and real-time secure data transmission via message advertisement. The algorithm uses a unique temporal encryption key to encrypt each transmitting packet with a simple function such as XOR. With small additional overhead on computational resources, DLS can significantly enhance security over existing baseline encryption algorithms. To evaluate its performance, the algorithm was utilized on beacon data encryption over advertising channels. The experiments demonstrated the use of the DLS encryption algorithm on top of various light-weight symmetric encryption algorithms (i.e., TEA, XTEA, PRESENT) and a MD5 hash function. The experimental results show that DLS can achieve acceptable results for avalanche effect, key sensitivity, and randomness in ciphertexts with a marginal increase in the resource usage. The proposed DLS encryption algorithm is suitable for implementation at the application layer, is light and energy efficient, reduces/removes the need for secret key exchange between sensor nodes and the server, is applicable to dynamic message size, and also protects against attacks such as known plaintext attack, brute-force attack, replaying attack, and differential attack.

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Practical fall detection based on IoT technologies: A survey

Cited by 45 publications

References 95 publications

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

A review of remote health monitoring based on internet of things

A Dynamic Light-Weight Symmetric Encryption Algorithm for Secure Data Transmission via BLE Beacons

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