Wearable band for hand gesture recognition based on strain sensors

Ferrone, Andrea; Maita, Francesco; Maiolo, Luca; Arquilla, M.; Castiello, Andrea; Pecora, A.; Jiang, Xianta; Menon, Carlo; Colace, Lorenzo

doi:10.1109/biorob.2016.7523814

Cited by 48 publications

(22 citation statements)

References 8 publications

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“…In this paper, we investigated the feasibility of a wearable textile-based stretch sensor to detect if someone is talking. The stretch sensor has been fabricated in our research lab (Menrva) at Simon Fraser University, Canada [ 18 ], using a mixture of polymer and conductive carbon black. The sensor shows similar properties as the commercially available sensors from Adafruit (New York, NY, USA) [ 19 ] and Image SI (Staten Island, NY, USA) [ 20 ], but only has a diameter of 0.4 mm, which makes it suitable to integrate into garments ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Ejupi

Menon

2018

Sensors

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Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness.

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Section: Methodsmentioning

confidence: 99%

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Ejupi

Menon

2018

Sensors

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“…WristFlicker supports both discrete and continuous input, and can be used in an eyes-free manner. Recently, Ferrone et al [47] developed a wristband equipped with stretchable polymeric strain gauge sensors to detect wrist movements. The number of small filaments (0.7 mm diameter and 1 cm length) are distributed on the surface of the wristband at regular intervals to cover the muscles of interest.…”

Section: Hand-worn Clothing-based Interfacesmentioning

confidence: 99%

Design of Interactions for Handheld Augmented Reality Devices Using Wearable Smart Textiles: Findings from a User Elicitation Study

Nanjappan

Shi

et al. 2019

Applied Sciences

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Advanced developments in handheld devices' interactive 3D graphics capabilities, processing power, and cloud computing have provided great potential for handheld augmented reality (HAR) applications, which allow users to access digital information anytime, anywhere. Nevertheless, existing interaction methods are still confined to the touch display, device camera, and built-in sensors of these handheld devices, which suffer from obtrusive interactions with AR content. Wearable fabric-based interfaces promote subtle, natural, and eyes-free interactions which are needed when performing interactions in dynamic environments. Prior studies explored the possibilities of using fabric-based wearable interfaces for head-mounted AR display (HMD) devices. The interface metaphors of HMD AR devices are inadequate for handheld AR devices as a typical HAR application require users to use only one hand to perform interactions. In this paper, we aim to investigate the use of a fabric-based wearable device as an alternative interface option for performing interactions with HAR applications. We elicited user-preferred gestures which are socially acceptable and comfortable to use for HAR devices. We also derived an interaction vocabulary of the wrist and thumb-to-index touch gestures, and present broader design guidelines for fabric-based wearable interfaces for handheld augmented reality applications. Appl. Sci. 2019, 9, 3177 2 of 21 and portable enough to be carried wherever users go. With this ubiquitous availability, HAR allows us to develop and design innovative applications in navigation, education, gaming, tourism, interactive shopping, production, marketing, and others [3]. Thus, smartphones have been identified as an ideal platform for HAR experiences in various outdoor and indoor environments [4][5][6].In order to interact with the virtual world using HAR displays, a user needs to position and orientate the device using one hand and manipulate the virtual 3D objects with the other hand. In general, the touchscreen is used as a primary interface to interact with AR content [7,8]. In addition, the various built-in sensors in the handheld devices-such as cameras, GPS, compass, accelerometers, and gyroscope-enable to precisely determine the position and orientation of the device in the real world (e.g., [8][9][10]). Furthermore, the device's camera is used to naturally capture the user's mid-air hand movements while holding the device [11,12].Like in HMD AR, manipulations such as selecting and moving virtual 3D information are primary interactions in HAR devices [13]. The existing HAR interaction methods, such as touch input, offer promising solutions to manipulate virtual content (e.g., [14]). However, they still have substantial limitations. For instance, touch input is limited by the device's physical boundary and usability suffers as on-screen content becomes occluded by finger (i.e., finger occlusions [15,16]). Also, 2D inputs on the touch surface do not directly support manipulating the six degrees of freedom of a virt...

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“…Even in different applications especially in biomedical market, wearable technology and prototypes could exploit the solutions proposed in this work to overcome several issue related to device durability, comfort, and contact force detection [47].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Systemic Approach for the Definition of a Safer Human-Robot Interaction

Pecora

Maiolo

Minotti

et al. 2019

Factories of the Future

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Smart factories must speed up their processes to face new manufacturing challenges and, at the same time, demonstrate an extremely high degree of flexibility to reduce production costs and time. This kind of issues can be addressed by the cooperation between humans and robots in a mixed human-robot working environment. Robots have the compelling advantage of spatial precision and repeatability as well as the capability of applying defined forces. Humans, on the other hand, are especially skilled at complex manipulations and adapting to changing task requirements. In this complicate scenario of co-shared workplace and continuous human-robot interaction, safety strategies are a key requirement to avoid possible injuries to humans or fatal accidents. This chapter proposes a systemic approach to respond to these requirements. The approach merges and manages multiple sensing sources, redundant transmission protocols and software decision mechanisms, aiming to guarantee a continuous and reconfigurable co-share scenario that enables an operative interaction between human workers and robots in a controlled and safe environment. Furthermore, new technological solutions and innovative methodologies are presented for the definition of a safer workplace in human-robot interaction scenarios. 8.1 Scientific and Industrial Motivations The need to face the production of complex families of product, with short life cycles, often characterized by strongly variable production patterns, requires factories to be able to quickly evolve and reconfigure [1]. These reconfigurable factories

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Wearable band for hand gesture recognition based on strain sensors

Cited by 48 publications

References 8 publications

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Design of Interactions for Handheld Augmented Reality Devices Using Wearable Smart Textiles: Findings from a User Elicitation Study

Systemic Approach for the Definition of a Safer Human-Robot Interaction

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