A smart watch-based gesture recognition system for assisting people with visual impairments

Porzi, Lorenzo; Messelodi, Stefano; Modena, Carla Mara; Ricci, Elisa

doi:10.1145/2505483.2505487

Cited by 60 publications

(46 citation statements)

References 15 publications

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“…In this context, data collection is less intrusive and the research achievements seem to be closer to the real world, since proposed solutions can be applied in daily situations. Therefore, some works in the literature focus on mobile activity recognition due to its applicability in many domains [10], [8], [19], [2], [7], [13]. Many of these application domains of activity recognition were summarized by Lockhart et al [10] that described and categorized a variety of applications based on mobile activity recognition, aiming to direct and encourage other works in the area.…”

Section: Related Workmentioning

confidence: 99%

“…Among these works, Porzi et al [13] presented a first prototype of a low-cost system to help visually impaired people, based on the use of a smartphone and a smartwatch. The data from smartwatch sensors is used as input to a gesture recognition algorithm, which runs in the smartphone.…”

Section: Related Workmentioning

confidence: 99%

“…Among the marketable wearable devices that can be used as data collection tool, stand out the smartwatches, because they are cheap and nonintrusive devices [13], can be worn 24 hours a day and be water resistant [2], and generally their battery life is more durable than smartphone ones [3]. Thus, they allow a less intrusive way to monitor physical activities and, consequently, the development of innovative applications [11], such as unsafe driving detection [9], the monitoring of user daily activities [16], and the assistance to people with visual impairments [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Ramos

Lorayne²,

Costa

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Activity recognition has been widely studied in ubiquitous computing since it can be used in several application domains, such as fall detection and gesture recognition. Initially, works in this area were based on research-only devices (bodyworn sensors). However, with advances in mobile computing, current research focuses on mobile devices, mainly, smartphones. These devices provide Internet access, processing, and various sensors, such as accelerometer and gyroscope, which are useful resources for activity recognition. Therefore, many studies use smartphones as data source. Additionally, some works have already considered the use of wristbands and specially-designed watches, but fewer investigate the latest marketable wearable devices, such as smartwatches, which are less intrusive and can provide new opportunities to complement smartphone data. Moreover, for the best of our knowledge, no previous work experimentally evaluates the impact caused by the combination of sensor data from smartwatches and smartphones on the accuracy of activity recognition approaches. Therefore, the main goal of this experimental evaluation is to compare the use of data from smartphones as well as the combination of data from smartphones and smartwatches for activity recognition. We evidenced that the use of smartphone and smartwatch data combined can increase the accuracy of activity recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Ramos

Lorayne²,

Costa

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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show abstract

“…Moreover, it is worth noting that Wi-Fi and Bluetooth technologies can also be used as environmental metrics able to study mobility patterns [9] and locale characteristics [10]. Several other studies have been based on the use of sensors such as microphones [11] and cameras [12] as a means of automatically recognizing particular places, or simply for identifying the context that users find themselves in. However, analysis of audio traces requires microphones to record continuously, raising privacy issues.…”

Section: Environmental Metricsmentioning

confidence: 99%

Understanding user daily mobility using mobile and wearable sensing systems

Faye

Engel

2016

2016 International Conference on Information and Communication Technology Convergence (ICTC)

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Abstract-Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allow us to assess human behavior and movement at different levels. In this article, we focus on the concept of human mobility. With the participation of 13 individuals, we carried out an experiment to discover how groups of sensors currently available in smartphones and smartwatches can help to distinguish different profiles and patterns of human mobility. We show that it is possible to use not only motion sensors but also physiological sensors and environmental data provided, for instance, by Wi-Fi. Finally, detailed study of these categories enables us to offer a way of representing the mobility of individual users, based on anonymized traces and graph theory.

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“…Shin et al [18] study patients with mental disorders and use smartwatches to help quantify the exercise and the amount of sunlight wearers have received, using GPS, accelerometer and the light sensor. Video sensing also permits various activities to be recognized [19]. However, video analysis is both algorithmically and computationally expensive, especially in a resource-constrained environment.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

Faye

Frank

Engel

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today's smartphones have the potential to monitor and record a complete history of their owners' movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect di↵erent activities and associated physiological patterns. Next, the use of a scalable and energy-e cient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence.

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A smart watch-based gesture recognition system for assisting people with visual impairments

Cited by 60 publications

References 15 publications

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Understanding user daily mobility using mobile and wearable sensing systems

Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

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