A Mobile Scanner for Probing Liquid Samples in Everyday Settings

Jiang, Weiwei; Marini, Gabriele; Berkel, Niels van; Sarsenbayeva, Zhanna; Luo, Chu; He, Xin; Dingler, Tilman; Kawahara, Yoshihiro; Kostakos, Vassilis

doi:10.1145/3267305.3274764

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…In addition to the use cases reporting professional NIRS methods, recent works in the HCI community show a great potential of introducing miniaturized NIRS in everyday decision-making tasks, including the aforementioned pill identification task [44] that can also be applied to food monitoring tasks, alcohol concentration estimation [52] and beverage identification tasks [36,37]. These examples highlight the potential of NIRS being widely used by non-experts in the near future [44,46].…”

Section: Foodmentioning

confidence: 99%

“…The collected spectra were used to develop machine learning models that can detect gluten. For scanning, we used the scanner settings recommended in prior literature [36,37,44]. Specifically, we adopted the Hadamard method with a wavelength range from 900 nm to 1700 nm, 7.03 nm generated light pattern width, 228 digital resolution, 0.635 ms exposure time, and 6 repeated scans for averaging.…”

Section: Gluten Classifiermentioning

confidence: 99%

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User Trust in Assisted Decision-Making Using Miniaturized Near-Infrared Spectroscopy

Jiang

Sarsenbayeva

Berkel

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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We investigate the use of a miniaturized Near-Infrared Spectroscopy (NIRS) device in an assisted decision-making task. We consider the real-world scenario of determining whether food contains gluten, and we investigate how end-users interact with our NIRS detection device to ultimately make this judgment. In particular, we explore the effects of different nutrition labels and representations of confidence on participants' perception and trust. Our results show that participants tend to be conservative in their judgment and are willing to trust the device in the absence of understandable label information. We further identify strategies to increase user trust in the system. Our work contributes to the growing body of knowledge on how NIRS can be mass-appropriated for everyday sensing tasks, and how to enhance the trustworthiness of assisted decision-making systems. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile devices.

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Section: Foodmentioning

confidence: 99%

Section: Gluten Classifiermentioning

confidence: 99%

User Trust in Assisted Decision-Making Using Miniaturized Near-Infrared Spectroscopy

Jiang

Sarsenbayeva

Berkel

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…This spectrum can be regarded as a unique chemical "fingerprint" for the object, from which ingredient information can be extracted. However, recent studies in the HCI community suggest that it is not straightforward to use the miniaturized NIRS technology in practice [4,5,9]. For example, Klakegg et al highlighted various challenges for non-experts to take advantage of miniaturized NIRS, including spectrum distortions as the result of user-induced errors [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Their study can be generalized to solid objects, such as estimating the maturity of fruits, ranking beef, etc. For the identification of non-solid objects, Jiang et al extended the study to identify liquids such as everyday drinks and liquors using a customized 3D printed clamp and software [4,5].…”

Section: Introductionmentioning

confidence: 99%

UbiNIRS: A Software Framework for Miniaturized NIRS-based Applications

Jiang¹,

Sarsenbayeva²,

Yu³

et al. 2021

Preprint

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We present UbiNIRS, a software framework for rapid development and deployment of applications using miniaturized nearinfrared spectroscopy (NIRS). NIRS is an emerging material sensing technology that has shown a great potential in recent work from the HCI community such as in situ pill testing. However, existing methods require significant programming efforts and professional knowledge of NIRS, and hence, challenge the creation of new NIRS-based applications. Our system helps to resolve this issue by providing a generic server and a mobile app, using the best practices for NIRS applications in literature. The server creates and manages UbiNIRS instances without the need for any coding or professional knowledge of NIRS. The mobile app can register multiple UbiNIRS instances by communicating with the server for different NIRS-based applications. Furthermore, UbiNIRS enables NIRS spectrum crowdsourcing for building a knowledge-base.

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