A Survey on Human Activity Recognition Based on Temporal Signals of Portable Inertial Sensors

Elbasiony, Reda; Gomaa, Walid

doi:10.1007/978-3-030-14118-9_72

Cited by 21 publications

(10 citation statements)

References 39 publications

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“…In addition, HAR, which includes camera-based sensors and systems that combine wearable and ambient sensors, is presented. Sousa Lima et al (2019) provide a comprehensive, state-of-theart overview of the current HAR solution for smartphone inertial sensors and Elbasiony and Gomaa [14] (2019) include a thoroughness survey of several HAR systems in portable sensors (Accelerometer, Gyroscope and Magnetometer). D.Triboan et al [15] reports an approach to fine-grained AR which combines multimodal data from individual objects and addresses the imprecise nature of non-binary sensor measurements.…”

Section: Existing Surveysmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Kumar¹

2021

Preprint

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Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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Section: Existing Surveysmentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Kumar¹

2021

Preprint

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“…The findings of Ye et al [32] demonstrate that the activity of the wearer of a monitoring device which uses accelerometers to extract and filter patterns of movement can indicate that violence is occurring or likely to occur. A more recent and extensive study by Elbasiony et al [33] investigated whether normal human behaviors can be described through the use of recorded movements alone, a possibility also explored by smartphones in which accelerometers are embedded [34,35]. A further innovation was introduced by the monitoring solution proposed by Jesudoss et al [36], namely a tilt sensor.…”

Section: Abrupt Movementsmentioning

confidence: 99%

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

et al. 2020

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Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

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“…Ye et al [19] discovered that it is possible to detect expressions of violence thanks to the movements of the person being monitored, using accelerometers and extracting and filtering patterns. More recently, and using a wider application, Elbasiony et al [20] explored the possibility of describing ordinary activities and human behavior using only registered movements. This idea was also studied using smartphone accelerometers [21,22].…”

Section: Wearable Devices Structured In a Body Area Network (Ban) Envmentioning

confidence: 99%

Towards a Holistic ICT Platform for Protecting Intimate Partner Violence Survivors Based on the IoT Paradigm

et al. 2019

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Featured Application: A complete review of information and communication technology (ICT) strategies to manage intimate partner violence (IPV) and protect IPV survivors is provided. A holistic ICT solution which would overcome the limitations of previous works is presented, promoting symmetry in society.Abstract: Intimate partner violence (IPV) remains a scourge that compromises the rights of many women around the world, shaping an asymmetry in civil rights. Fighting gender-based violence, especially when it is committed by an intimate partner, is an important responsibility that needs to be addressed from all angles. It is also remarkable that our society is clearly conditioned by information and communication technology (ICT), which involves many aspects of our daily life. Unfortunately, violence that is performed in the real world is also replicated in this 'virtual' existence, by offenders in ICT contexts. On the other hand, the same technologies also provide a plethora of opportunities to fight IPV, which are enhanced by the innovative paradigm of the so-called Internet of Things (IoT). In this work, we first present a thorough compilation of ICT proposals already published-based on either hardware or software-aimed at protecting IPV survivors, and which can be applied in real life situations but also within social networks. The challenges that still lie ahead are highlighted and, a complete ICT-based platform for IPV management, within an IoT framework, that overcomes the limitations of previous works is proposed, and then promoting a symmetry between individuals in society.Symmetry 2020, 12, 37 2 of 17 in any form or means. According to UN (United Nations) statistics, almost 35% women around the world have experienced some kind of physical or sexual violence [1]. The same statistics show that some 75% of women face physical and sexual aggression. This is a call for attention to be paid to this scourge. It is incredible that in 2017, some 87,000 women were killed across the world, of whom 58% (50,000) were killed by their husband or other relatives (https://www.unodc.org/).In recent years much research has focused on IPV and its connection to many related issues, that is, social awareness [2]. This wide scope includes resources used by the victims (from now on, more appropriately called 'survivors') [3], barriers, and formal assistance in facing different expressions of violence [4]. Nonetheless, some authors like Bruckman have noted that the way violence is developed and its impact on women in an environment characterized by the use of information and communication technology (ICT), such as mobile phones, social media, or generally using internet, has not been properly studied or documented [5], although in recent years it has become an issue of study [6].In 2019, ICT and especially the internet, have clearly advanced in every aspect of society, and have had an effect in every part of the world. In the early 1990s, Haraway [7] anticipated the social changes and the effect, especially in gender issues, that...

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A Survey on Human Activity Recognition Based on Temporal Signals of Portable Inertial Sensors

Cited by 21 publications

References 39 publications

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

Towards a Holistic ICT Platform for Protecting Intimate Partner Violence Survivors Based on the IoT Paradigm

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