Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications

Kos, Anton; Milutinović, Veljko; Umek, Anton

doi:10.1016/j.future.2018.03.032

Cited by 71 publications

(33 citation statements)

References 25 publications

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“…In addition to the basic functional modulates such as signal acquisition and processing by sensors and computation components, [19,23,975] the capability of short-range wireless communication is another essential aspect for future human-integrated devices, which could effectively transmit data to a portable platform like smartphones for real-time monitoring. [976,977] The continuous interaction between wearable sensors and portable smart platforms could enable long-term monitoring of vital signals from the human body. The International Telecommunication Union (ITU) defines certain radio bands as industrial, scientific, and medical (ISM) bands.…”

Section: Wireless Communication Devicesmentioning

confidence: 99%

Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables

2020

Advanced Intelligent Systems

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Human-integrated devices include wearable and implantable devices for monitoring vital human signals or interacting with the human body. [1-3] The human-integrated devices needed to be tethered to the organs or the human skin for implementing their functions such as sensing and energy harvesting. [4-18] The economical, agile, customizable manufacturing, and integration of multifunctional device modules into networked systems with mechanical compliance and robustness will enable unprecedented human-integrated smart wearables [19-23] and usher in exciting opportunities in emerging technologies such as electronics, [24-29] wearable computation, [30-36] human-machine interface, [37-42] robotics, [43-48] and advanced healthcare. [49-54] The fabrication of stateof-the-art microelectronics involves hightemperature processing steps, which are generally incompatible with the flexible substrates (e.g., paper, polymer, fiber, etc.) [55-61] widely used in the wearable devices. Moreover, the current microfabrication technologies are not well positioned to process the emerging functional nanomaterials (e.g., nanowires [NWs] and 2D materials). [62-65] Such incomparability severely limits the pace of deploying these emerging materials (despite their unprecedented properties) in wearable and flexible device technologies. The additive manufacturing (AM) processes have emerged as potential candidates for addressing the earlier limitations of the current microfabrication technologies in fabricating flexible/wearable printed devices with diversified functionalities, e.g., energy harvesting/storage, sensing, actuation, and computation, leveraging advantages such as maskless processing, versatile ink formulation, low cost, low processing temperature, and scalability. [66-71] Researchers have devoted tremendous efforts to develop the related technologies, and several reviews have provided excellent discussions on the material formulation and process aspects of AM techniques. [63,72-79] However, to our best knowledge, there are few review reports about the ink-based additive nanomanufacturing of functional materials for human-integrated smart wearables. To fill this gap, here we review the recent progress in ink-based

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Section: Wireless Communication Devicesmentioning

confidence: 99%

Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables

2020

Advanced Intelligent Systems

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“…Anywhere, anything, anytime, and anyone are the A4 demands important to physicians. We note that A4 and C4 also contain mutual interests [52].…”

Section: Future Generation Of Wearable Systems (G 4 ): Challenges Andmentioning

confidence: 99%

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

Haghi

Deserno

2020

Chemosensors

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We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated.

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“…Robot-assisted training in combination with virtual reality is becoming increasingly popular for facilitating motor learning. Tremendous efforts have been undertaken to (i) develop controllable and appealing training conditions [for examples, see overviews in the field of surgery by Bartlett et al (1) and in the field of sport by Neumann et al (2)], (ii) design augmented feedback to foster active participation and guide the trainee [for an overview, see Sigrist et al (3)], and (iii) comprehensively monitor task performance by sensors of the robot or additional wearables [e.g., summarized by Kos et al (4)].…”

Section: Introductionmentioning

confidence: 99%

When a robot teaches humans: Automated feedback selection accelerates motor learning

et al. 2019

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A multitude of robotic systems have been developed to foster motor learning. Some of these robotic systems featured augmented visual or haptic feedback, which was automatically adjusted to the trainee’s performance. However, selecting the type of feedback to achieve the training goal usually remained up to a human trainer. We automated this feedback selection within a robotic rowing simulator: Four spatial errors and one velocity error were considered, all related to trunk-arm sweep rowing set as the training goal to be learned. In an alternating sequence of assessments without augmented feedback and training sessions with augmented, concurrent feedback, the experimental group received feedback, thus addressing the main shortcoming of the previous assessment. With this approach, each participant of the experimental group received an individual sequence of 10 training sessions with feedback. The training sequences from participants in the experimental group were consecutively applied for participants in the control group. Both groups were able to reduce spatial and velocity errors due to training. The learning rate of the requested velocity profile was significantly higher for the experimental group compared with the control group. Thus, our robotic rowing simulator accelerated motor learning by automated feedback selection. This demonstration of a working, closed-loop selection of types of feedback, i.e., training conditions, could serve as the basis for other robotic trainers incorporating further human expertise and artificial intelligence.

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Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications

Cited by 71 publications

References 25 publications

Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables

Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

When a robot teaches humans: Automated feedback selection accelerates motor learning

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