Practical fall detection based on IoT technologies: A survey

Djelouat, 2017, Cs-based fall detection for connected health applications, 1

Zhu, 2015, A survey of fall detection algorithm for elderly health monitoring, 270

Schepers, 2017, Pedestrian falls: a review of the literature and future research directions, J. Safe. Res., 62, 227, 10.1016/j.jsr.2017.06.020

Pasluosta, 2015, Parkinson’s disease as a working model for global healthcare restructuration: the internet of things and wearables technologies, 162

Paul, 2016, Two-year trajectory of fall risk in people with parkinson disease: a latent class analysis, Arch. Phys. Med. Rehabil., 97, 372, 10.1016/j.apmr.2015.10.105

de Lima, 2017, Freezing of gait and fall detection in parkinson’s disease using wearable sensors: a systematic review, J. Neurol., 264, 1642, 10.1007/s00415-017-8424-0

Lu, 2019, Deep learning for fall detection: three-dimensional cnn combined with lstm on video kinematic data, IEEE J. Biomed. Health Informat., 23, 314, 10.1109/JBHI.2018.2808281

Mistikoglu, 2015, Decision tree analysis of construction fall accidents involving roofers, Expert Syst. Appl., 42, 2256, 10.1016/j.eswa.2014.10.009

Fang, 2017, Accelerometer-based fall-portent detection algorithm for construction tiling operation, Autom. Constr., 84, 214, 10.1016/j.autcon.2017.09.015

Kanan, 2018, An iot-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies, Autom. Constr., 88, 73, 10.1016/j.autcon.2017.12.033

Wang, 2016, Pedestrian fall action detection and alarm in video surveillance, 502

Hsu, 2015, Development of a vision based pedestrian fall detection system with back propagation neural network, 433

Chu, 2018, A laypunov base fuzzy expert knowledge model identify elders fall signal, 1

de la Concepción, 2017, Mobile activity recognition and fall detection system for elderly people using ameva algorithm, Pervasive Mobile Comput., 34, 3, 10.1016/j.pmcj.2016.05.002

Li, 2014, Fall perception for elderly care: a fall detection algorithm in smart wristlet mhealth system, 4270

Li, 2016, Elderly taiwanese’s intrinsic risk factors for fall-related injuries, Int. J. Gerontol., 10, 137, 10.1016/j.ijge.2015.10.006

Shen, 2015, The implementation of a smartphone-based fall detection system using a high-level fuzzy petri net, Appl. Soft Comput., 26, 390, 10.1016/j.asoc.2014.10.028

Bourke, 2008, The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls, Med. Eng. Phys., 30, 937, 10.1016/j.medengphy.2007.12.003

Ahmad, 2015, Perception study on leading factors of slip and fall incidents in manufacturing industry, Proc. Manuf., 2, 263

Saadeh, 2017, A high accuracy and low latency patient-specific wearable fall detection system, 441

Liang, 2012, Pre-impact & impact detection of falls using wireless body sensor network, 763

Nyan, 2008, A wearable system for pre-impact fall detection, J. Biomech., 41, 3475, 10.1016/j.jbiomech.2008.08.009

Baker, 2017, Internet of things for smart healthcare: technologies, challenges, and opportunities, IEEE Access, 5, 26521, 10.1109/ACCESS.2017.2775180

Santos, 2016, An iot-based mobile gateway for intelligent personal assistants on mobile health environments, J. Netw. Comput. Appl., 71, 194, 10.1016/j.jnca.2016.03.014

Gia, 2018, Energy efficient wearable sensor node for iot-based fall detection systems, Microprocess. Microsyst., 56, 34, 10.1016/j.micpro.2017.10.014

El Zouka, 2019, Secure iot communications for smart healthcare monitoring system, Internet Things, 10.1016/j.iot.2019.01.003

Mass, 2019, Edge process management: a case study on adaptive task scheduling in mobile iot, Internet Things, 6, 100051, 10.1016/j.iot.2019.100051

Bittencourt, 2018, The internet of things, fog and cloud continuum: integration and challenges, Internet Things, 3–4, 134, 10.1016/j.iot.2018.09.005

Sinnapolu, 2018, Integrating wearables with cloud-based communication for health monitoring and emergency assistance, Internet of Things, 1, 40, 10.1016/j.iot.2018.08.004

Hosseinzadeh Sahraei, 2019, Efficient job scheduling in cloud computing based on genetic algorithm, Int. J. Commun. Netw. Distrib. Syst., 22, 447, 10.1504/IJCNDS.2019.099968

Gravina, 2017, Cloud-based activity-aaservice cyber–physical framework for human activity monitoring in mobility, Future Generat. Comput. Syst., 75, 158, 10.1016/j.future.2016.09.006

Mubashir, 2013, A survey on fall detection: principles and approaches, Neurocomputing, 100, 144, 10.1016/j.neucom.2011.09.037

Delahoz, 2014, Survey on fall detection and fall prevention using wearable and external sensors, Sensors, 14, 19806, 10.3390/s141019806

Xu, 2018, New advances and challenges of fall detection systems: a survey, Appl. Sci., 8, 418, 10.3390/app8030418

Pannurat, 2014, Automatic fall monitoring: a review, Sensors, 14, 12900, 10.3390/s140712900

Kurniawan, 2016, A wearable device for fall detection elderly people using tri dimensional accelerometer, 671

Swaroop, 2019, A health monitoring system for vital signs using iot, Internet Things, 5, 116, 10.1016/j.iot.2019.01.004

Li, 2011, Hilbert-huang transform for analysis of heart rate variability in cardiac health, IEEE/ACM Trans. Comput. Biol. Bioinformat. (TCBB), 8, 1557, 10.1109/TCBB.2011.43

Wang, 2017, Wifall: Device-free fall detection by wireless networks, IEEE Trans. Mobile Comput., 16, 581, 10.1109/TMC.2016.2557792

Cippitelli, 2017, Radar and rgb-depth sensors for fall detection: a review, IEEE Sens. J., 17, 3585, 10.1109/JSEN.2017.2697077

Akagündüz, 2017, Silhouette orientation volumes for efficient fall detection in depth videos, IEEE J. Biomed. Health Informat., 21, 756, 10.1109/JBHI.2016.2570300

Kepski, 2017, Event-driven system for fall detection using body-worn accelerometer and depth sensor, IET Comput. Vis., 12, 48, 10.1049/iet-cvi.2017.0119

Bian, 2015, Fall detection based on body part tracking using a depth camera, IEEE J. Biomed. Health informat., 19, 430, 10.1109/JBHI.2014.2319372

Lee, 2011, Semi-markov conditional random fields for accelerometer-based activity recognition, Appl. Intell., 35, 226, 10.1007/s10489-010-0216-5

Pierleoni, 2015, A high reliability wearable device for elderly fall detection, IEEE Sens. J., 15, 4544, 10.1109/JSEN.2015.2423562

Athavale, 2017, Biosignal monitoring using wearables: observations and opportunities, Biomed. Signal Process. Control, 38, 22, 10.1016/j.bspc.2017.03.011

Kikhia, 2016, Utilizing a wristband sensor to measure the stress level for people with dementia, Sensors, 16, 1989, 10.3390/s16121989

Kumar, 2014, Novel fall detection algorithm for the elderly people, 1

Bulling, 2011, Eye movement analysis for activity recognition using electrooculography, IEEE Trans. Pattern Anal. Mach. Intell., 33, 741, 10.1109/TPAMI.2010.86

Terroso, 2013, Active assistance for senior healthcare: a wearable system for fall detection, 1

Cheffena, 2016, Fall detection using smartphone audio features, IEEE J. Biomed. Health Informat., 20, 1073, 10.1109/JBHI.2015.2425932

Irtaza, 2017, A framework for fall detection of elderly people by analyzing environmental sounds through acoustic local ternary patterns, 1558

Li, 2012, A microphone array system for automatic fall detection, IEEE Trans. Biomed. Eng., 59, 1291, 10.1109/TBME.2012.2186449

Doukas, 2011, Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components, IEEE Trans. Inf. Technol. Biomed., 15, 277, 10.1109/TITB.2010.2091140

Li, 2010, Acoustic fall detection using a circular microphone array, 2242

Popescu, 2009, Acoustic fall detection using one-class classifiers, 3505

Gu, 2018, Locomotion activity recognition using stacked denoising autoencoders, IEEE Internet Things J., 5, 2085, 10.1109/JIOT.2018.2823084

Ozcan, 2017, Autonomous fall detection with wearable cameras by using relative entropy distance measure., IEEE Trans. Hum.-Mach. Syst., 47, 31

Ozcan, 2016, Wearable camera-and accelerometer-based fall detection on portable devices, IEEE Embedded Syst. Lett., 8, 6, 10.1109/LES.2015.2487241

Ozcan, 2013, Automatic fall detection and activity classification by a wearable embedded smart camera, IEEE J. Emerg. Select. Top. Circuits Syst., 3, 125, 10.1109/JETCAS.2013.2256832

Stone, 2015, Fall detection in homes of older adults using the microsoft kinect., IEEE J. Biomed. Health Informat., 19, 290, 10.1109/JBHI.2014.2312180

Wang, 2017, Rt-fall: a real-time and contactless fall detection system with commodity wifi devices., IEEE Trans. Mob. Comput., 16, 511, 10.1109/TMC.2016.2557795

Rezazadeh, 2018, Novel ibeacon placement for indoor positioning in iot, IEEE Sens. J., 18, 10240, 10.1109/JSEN.2018.2875037

Khan, 2018, On the correlation of sensor location and human activity recognition in body area networks (bans), IEEE Syst. J., 12, 82, 10.1109/JSYST.2016.2610188

Kau, 2015, A smart phone-based pocket fall accident detection, positioning, and rescue system, IEEE J. Biomed. Health Informat., 19, 44, 10.1109/JBHI.2014.2328593

Sannino, 2015, A supervised approach to automatically extract a set of rules to support fall detection in an mhealth system, Appl. Soft Comput., 34, 205, 10.1016/j.asoc.2015.04.060

Rakhman, 2014, Fall detection system using accelerometer and gyroscope based on smartphone, 99

Abbate, 2012, A smartphone-based fall detection system, Pervasive Mobile Comput., 8, 883, 10.1016/j.pmcj.2012.08.003

Soewito, 2015, Fall detection algorithm to generate security alert, Proc. Comput. Sci., 59, 350, 10.1016/j.procs.2015.07.532

De Cillis, 2015, Fall-detection solution for mobile platforms using accelerometer and gyroscope data, 3727

Kostopoulos, 2016, F2d: a location aware fall detection system tested with real data from daily life of elderly people, Proc. Comput. Sci., 98, 212, 10.1016/j.procs.2016.09.035

Maglogiannis, 2014, Fall detection using commodity smart watch and smart phone, 70

Guo, 2015, A threshold-based algorithm of fall detection using a wearable device with tri-axial accelerometer and gyroscope, 54

Rezazadeh, 2018, A location-based smart shopping system with iot technology, 748

Nari, 2016, A simple design of wearable device for fall detection with accelerometer and gyroscope, 88

Benocci, 2010, Accelerometer-based fall detection using optimized zigbee data streaming, Microelectron. J., 41, 703, 10.1016/j.mejo.2010.06.014

Rezazadeh, 2015, Impact of static trajectories on localization in wireless sensor networks, Wirel. Netw., 21, 809, 10.1007/s11276-014-0821-z

Hakim, 2017, Smartphone based data mining for fall detection: analysis and design, Proc. Comput. Sci., 105, 46, 10.1016/j.procs.2017.01.188

Javad Rezazadeh, 2012, Message-efficient localization in mobile wireless sensor networks, J. Commun. Comput., 9, 340

Rezazadeh, 2014, Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks, IEEE Sens. J., 14, 3052, 10.1109/JSEN.2014.2322958

Gia, 2016, Iot-based fall detection system with energy efficient sensor nodes, 1

Abbasian Dehkordi, 2019, A survey on data aggregation techniques in iot sensor networks, Wireless Netw., 1

Sattarian, 2019, Indoor navigation systems based on data mining techniques in internet of things: a survey, Wireless. Netw., 25, 1385, 10.1007/s11276-018-1766-4

Lashkari, 2019, Crowdsourcing and sensing for indoor localization in iot: a review, IEEE Sens. J., 19, 2408, 10.1109/JSEN.2018.2880180

Moradi, 2012, A reverse localization scheme for underwater acoustic sensor networks, Sensors, 12, 4352, 10.3390/s120404352

Rezazadeh, 2012, Mobile wireless sensor networks overview, Int. J. Comput. Commun. Netw., 2, 17

Rezazadeh, 2011, Efficient localization via middle-node cooperation in wireless sensor networks, 410

Farahzadi, 2017, Middleware technologies for cloud of things-a survey, Digit. Commun. Netw., 4, 176, 10.1016/j.dcan.2017.04.005

Rezazadeh, 2019, Transmission power adjustment scheme for mobile beacon-assisted sensor localization, IEEE Trans. Ind. Informat., 15, 2859, 10.1109/TII.2018.2868837

Laplante, 2018, Stakeholder identification and use case representation for internet-of-things applications in healthcare, IEEE Syst. J., 12, 1589, 10.1109/JSYST.2016.2558449

Yang, 2016, Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit, Autom. Constr., 68, 194, 10.1016/j.autcon.2016.04.007

Tong, 2013, Hmm-based human fall detection and prediction method using tri-axial accelerometer, IEEE Sens. J., 13, 1849, 10.1109/JSEN.2013.2245231

Catarinucci, 2015, An iot-aware architecture for smart healthcare systems, IEEE Internet Things J., 2, 515, 10.1109/JIOT.2015.2417684

Lugade, 2011, Fall risk analysis using k-means clustering on gait balance measures

Malazi, 2018, Combining emerging patterns with random forest for complex activity recognition in smart homes, Appl. Intell., 48, 315, 10.1007/s10489-017-0976-2

Zanella, 2014, Internet of things for smart cities, IEEE Internet Things J., 1, 22, 10.1109/JIOT.2014.2306328

Shen, 2017, A novel fall prediction system on smartphones, IEEE Sens. J., 17, 1865, 10.1109/JSEN.2016.2598524

Howcroft, 2017, Prospective fall-risk prediction models for older adults based on wearable sensors, IEEE Trans. Neural Syst. Rehabil. Eng., 25, 10.1109/TNSRE.2017.2687100

Annese, 2015, Gait analysis for fall prediction using emg triggered movement related potentials, 1

Shi, 2016, Algorithm of the fall prediction based on the double foot pressure and micro inertial sensors, 354

Song, 2017, Histogram based fall prediction of patients using a thermal imagery camera, 161

Alazrai, 2015, A fall prediction methodology for elderly based on a depth camera, 4990

Kumar, 2017, Architecture for iot based geriatric care fall detection and prevention, 1099

Chaccour, 2017, From fall detection to fall prevention: a generic classification of fall-related systems, IEEE Sens. J., 17, 812, 10.1109/JSEN.2016.2628099

Koo, 2016, An internet-of-things (iot) system development and implementation for bathroom safety enhancement, Proc. Eng., 145, 396, 10.1016/j.proeng.2016.04.004

Tamura, 2009, A wearable airbag to prevent fall injuries, IEEE Trans. Inf. Technol. Biomed., 13, 910, 10.1109/TITB.2009.2033673

Lee, 2015, Inertial sensing-based pre-impact detection of falls involving near-fall scenarios, IEEE Trans. Neural Syst. Rehabil. Eng., 23, 258, 10.1109/TNSRE.2014.2357806

Lara, 2013, A survey on human activity recognition using wearable sensors., IEEE Commun. Surv. Tutor., 15, 1192, 10.1109/SURV.2012.110112.00192

Yatsuda, 2019, A robot motion design scheme for watching the elderly based on human gesture sensing, Internet Things, 5, 168, 10.1016/j.iot.2019.01.006

Nakagawa, 2016, Tandem stance avoidance using adaptive and asymmetric admittance control for fall prevention, IEEE Trans. Neural Syst. Rehabil. Eng., 24, 542, 10.1109/TNSRE.2015.2429315

Di, 2016, Fall detection and prevention control using walking-aid cane robot, IEEE/ASME Trans. Mechatron., 21, 625, 10.1109/TMECH.2015.2477996

Nishdia, 2017, Living function resilient service using a mock living lab and real living labs: Development of balcony-iot and handrail-iot for healthcare, Proc. Comput. Sci., 113, 121, 10.1016/j.procs.2017.08.326

Grammatikis, 2018, Securing the internet of things: challenges, threats and solutions, Internet Things, 5, 41, 10.1016/j.iot.2018.11.003

Noel, 2017, Structural health monitoring using wireless sensor networks: a comprehensive survey, IEEE Commun. Surv. Tutor., 19, 1403, 10.1109/COMST.2017.2691551

Cornacchia, 2017, A survey on activity detection and classification using wearable sensors, IEEE Sens. J., 17, 386, 10.1109/JSEN.2016.2628346

Zhang, 2015, A survey on vision-based fall detection, 46

Hsieh, 2016, A machine learning approach to fall detection algorithm using wearable sensor, 707

Choi, 2011, A study on machine learning algorithms for fall detection and movement classification, 1

Rescio, 2013, Support vector machine for tri-axial accelerometer-based fall detector, 25

Luo, 2014, Fall detection using three wearable triaxial accelerometers and a decision-tree classifier, Biomed. Eng.: Appl., Basis Commun., 26, 1450059

Kerdegari, 2013, Development of wearable human fall detection system using multilayer perceptron neural network, Int. J. Comput. Intell. Syst., 6, 127, 10.1080/18756891.2013.761769

Erdogan, 2012, A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data, IET Commun., 6, 3281, 10.1049/iet-com.2011.0228

Jian, 2015, A portable fall detection and alerting system based on k-nn algorithm and remote medicine, China Commun., 12, 23, 10.1109/CC.2015.7114066

Rihana, 2016, Wearable fall detection system, 84

Horta, 2013, A mobile health application for falls detection and biofeedback monitoring, 36

Bhati, 2017, mhealth based ubiquitous fall detection for elderly people, 1

Zhou, 2014, A low-power, wireless, wrist-worn device for long time heart rate monitoring and fall detection, 33

Malheiros, 2017, Fall detection system and body positioning with heart rate monitoring, IEEE Latin Am. Trans., 15, 1021, 10.1109/TLA.2017.7932688

Rawashdeh, 2017, A knowledge-driven approach for activity recognition in smart homes based on activity profiling, Fut. Generat. Comput. Syst., 10.1016/j.future.2017.10.031

Greene, 2016, Iot-based fall detection for smart home environments

Droghini, 2018, Human fall detection by using an innovative floor acoustic sensor, 97

Dinh, 2009, Implementation of a physical activity monitoring system for the elderly people with built-in vital sign and fall detection, 1226

Cheng, 2013, Triaxial accelerometer-based fall detection method using a self-constructing cascade-adaboost-svm classifier, IEEE J. Biomed. Health Informat., 17, 411, 10.1109/JBHI.2012.2237034

Kerdegari, 2015, A pervasive neural network based fall detection system on smart phone, J. Ambient Intell. Smart Environ., 7, 221, 10.3233/AIS-150306

Nukala, 2014, An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ann) and support vector machine (svm) algorithms, Open J. Appl. Biosens., 3, 29, 10.4236/ojab.2014.34004

Yu, 2013, An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment, IEEE J. Biomed. Health informat., 17, 1002, 10.1109/JBHI.2013.2274479

Yang, 2014, mpadal: a joint local-and-global multi-view feature selection method for activity recognition, Appl. Intell., 41, 776, 10.1007/s10489-014-0566-5

Kambhampati, 2015, Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier, Healthc. Technol. Lett., 2, 101, 10.1049/htl.2015.0018

Li, 2015, An improved classification method for fall detection based on bayesian framework, 237

Jiang, 2016, An intelligent information forwarder for healthcare big data systems with distributed wearable sensors, IEEE Syst. J., 10, 1147, 10.1109/JSYST.2014.2308324

Hassan, 2018, A robust human activity recognition system using smartphone sensors and deep learning, Fut. Generat. Comput. Syst., 81, 307, 10.1016/j.future.2017.11.029

Mohammad, 2018, Primitive activity recognition from short sequences of sensory data, Appl. Intell., 48, 3748, 10.1007/s10489-018-1166-6