Survey of Smart Technologies for Fall Motion Detection: Techniques, Algorithms and Tools

Patsadu, Orasa; Nukoolkit, Chakarida; Watanapa, Bunthit

doi:10.1007/978-3-642-35076-4_13

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…With the increasing pervasiveness of technology throughout industries, the application and integration of smart technologies has become a main focus in wide variety of contexts, including business (Lee, 2012b), health home systems (Patsadu, Nukoolkit & Watanapa, 2012) retail store usage (Lee, 2012b), urban governance (Himmelreich, 2013), the context of design education (McCardle, 2002) or energy monitoring in hotels (Rogerson & Sims, 2012). In the particular context of tourism, the concept of smart technologies has equally received attention, as a dynamic and fast-changing market place that is forced to exploit the full potential of smart technologies as a means of innovation (Zach, Gretzel & Xiang, 2010) and competitive advantage (Buhalis & Law, 2008).…”

Section: Literature Review Smart Technologies In Tourism Smart Technomentioning

confidence: 99%

Smart technologies for personalized experiences: a case study in the hospitality domain

2015

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Smart technologies in tourism for personalized experiencesKey Words (for indexing and abstract services -up to 6 words):Personalization; experience; smart technology; best practice; case study; JEL classification L83 Word Count 8411Word Processing Program Name and Version Number:EndNote X5 AbstractThe advances in technology have led to the emergence of innovative technological smart solutions providing unprecedented opportunities for the tourism industry. With intensified competition in the tourism market place, it has become paramount for businesses to explore the potential of technologies not only to optimise existing processes but strategically use technology in the delivery of more meaningful and personalized experiences.Despite its importance, knowledge about the role of smart technology for personalized experiences is still limited in both tourism theory and practice. This study therefore aims to explore how tourism businesses can strategically implement smart technologies to facilitate the personalization of customer experiences. In employing a qualitative case study approach, this study makes a two-fold contribution by a) identifying requirements of smart technologies for experience creation, including information aggregation, ubiquitous mobile connectedness and real time synchronization and b) highlighting how smart technology leads to two levels of personalized tourism experiences. Document, page 3/24

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Section: Literature Review Smart Technologies In Tourism Smart Technomentioning

confidence: 99%

Smart technologies for personalized experiences: a case study in the hospitality domain

2015

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“…In their study, health and social care staff appear less informed and less convinced on the benefits of fall detection technologies. There are several other survey papers on fall detection [24,25,26,27,28,29] that address similar ideas and issues already covered in this section.…”

Section: Survey Of Existing Literature Review On Fall Detectionmentioning

confidence: 92%

Review of fall detection techniques: A data availability perspective

Khan

Hoey

2017

Medical Engineering & Physics

174

106

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A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. We conclude our paper by discussing several open research problems in the field and pointers for future research.

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“…In fact, the adaptability of these systems to changes in the supervised area is quite reduced as they are carefully adjusted and parameterized for a very specific scenario. Besides, the accuracy of the fall detection may be strongly determined by external and non-controllable conditions, such as the illumination, the occurrence of audio artifacts [ 50 ] or the presence of sudden visual obstacles, which induce the existence of “blind” spots where the patient cannot be adequately tracked [ 51 ]. In addition, the users may be reluctant to the constant visual surveillance that these systems perform as they can feel the risk of having their privacy compromised (in this sense, context-aware architectures using motion sensing input devices, such as that presented in [ 49 ], which is based on the use of the Microsoft Kinnect depth sensor, can be considered much less intrusive).…”

Section: A Classification Of Fall Detection Systems (Fdss): Advantmentioning

confidence: 99%

Analysis of Android Device-Based Solutions for Fall Detection

Casilari

Luque

Morón

2015

Sensors

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Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

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Survey of Smart Technologies for Fall Motion Detection: Techniques, Algorithms and Tools

Cited by 11 publications

References 18 publications

Smart technologies for personalized experiences: a case study in the hospitality domain

Smart technologies for personalized experiences: a case study in the hospitality domain

Review of fall detection techniques: A data availability perspective

Analysis of Android Device-Based Solutions for Fall Detection

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