Live demonstration: BioSleeve, a wearable hands-free gesture control interface

Assad, Chris; Karras, Jaakko T.; Rodríguez, Javier Martínez; Pivo, Eli; Huang, Calvin; Wolf, Michael; Pomerantz, Marc; Stoica, Adrian

doi:10.1109/icsens.2016.7808566

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…Many researchers have integrated haptic or auditory technology for navigational control, navigation assistance, or feedback of assistive mobility devices. Wearable devices such as the Jet Propulsion Laboratory BioSleeve [ 44 , 45 ], the wireless tongue drive system (TDS) to smartphone (iPhone) electric powered wheelchair (PWC; TDS to smartphone (iPhone) electric-PWC [TDS-iPhone-PWC]; [ 46 ]), and the MyoSuit [ 47 ] were designed using haptic technology for navigational control and aided mobility, respectively. The JPL BioSleeve is a wearable, hands-free gesture recognition interface that decodes as many as 20 discrete hand and finger gestures and can estimate the continuous pose of the arm.…”

Section: Resultsmentioning

confidence: 99%

“…This information is obtained from the environment and user via a means of communication (usually an interface). USIs can send and receive information from the user (individuals with some form of disability) to the mobility aid via a communication channel that could be wired or wireless, such as the JPL BioSleeve [ 44 , 45 ] and the TDS-iPhone-PWC interface ( Table 3 ) [ 46 ] that can wirelessly control a mobility aid, the P300-based BCI ( Table 2 ) [ 74 ] that controls a wheelchair via a wired USB channel, and the autonomous scooter navigation mobility aid [ 13 ] that connects its computing module to its hard unit via a wired USB medium or a wireless Bluetooth medium. With the help of IoMT, interconnectivity between mobile devices and their environment and the storage or retrieval of relevant information for control, better autonomy, and monitoring are possible.…”

Section: Resultsmentioning

confidence: 99%

“…Affordability, accurate detection of environmental sounds, avoidance of cognitive overload of the users, ease of use, weight of devices, and commercialization are important factors to be considered [ 15 , 20 , 21 , 31 , 43 ]. For instance, JPL BioSleeve [ 44 , 45 ], a very promising interface for decoding a large number of gestures (dynamic and static hand positions) at high accuracy, integrates IMU signals with EMG for gesture recognition. Its intended goal of gesture recognition with high accuracy was achieved.…”

Section: Discussionmentioning

confidence: 99%

“…Many of the reviewed technologies applied different machine learning tools (classifiers and algorithms) to help process the acquired information. An example is the SVM classifier used by JPL BioSleeve in the studies by Assad et al [ 44 ] and Wolf et al [ 45 ] to classify gesture patterns. It was able to achieve an accuracy as high as 96%; however, as stated by Anguita et al [ 86 ], its accuracy was dependent on the chosen model, presence of noise, and data size.…”

Section: Discussionmentioning

confidence: 99%

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Adaptability of Assistive Mobility Devices and the Role of the Internet of Medical Things: Comprehensive Review

Oladele¹,

Markus²,

Abu‐Mahfouz³

2021

JMIR Rehabil Assist Technol

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Background With the projected upsurge in the percentage of people with some form of disability, there has been a significant increase in the need for assistive mobility devices. However, for mobility aids to be effective, such devices should be adapted to the user’s needs. This can be achieved by improving the confidence of the acquired information (interaction between the user, the environment, and the device) following design specifications. Therefore, there is a need for literature review on the adaptability of assistive mobility devices. Objective In this study, we aim to review the adaptability of assistive mobility devices and the role of the internet of medical things in terms of the acquired information for assistive mobility devices. We review internet-enabled assistive mobility technologies and non–internet of things (IoT) assistive mobility devices. These technologies will provide awareness of the status of adaptive mobility technology and serve as a source and reference regarding information to health care professionals and researchers. Methods We performed a literature review search on the following databases of academic references and journals: Google Scholar, ScienceDirect, Institute of Electrical and Electronics Engineers, Springer, and websites of assistive mobility and foundations presenting studies on assistive mobility found through a generic Google search (including the World Health Organization website). The following keywords were used: assistive mobility OR assistive robots, assistive mobility devices, internet-enabled assistive mobility technologies, IoT Framework OR IoT Architecture AND for Healthcare, assisted navigation OR autonomous navigation, mobility AND aids OR devices, adaptability of assistive technology, adaptive mobility devices, pattern recognition, autonomous navigational systems, human-robot interfaces, motor rehabilitation devices, perception, and ambient assisted living. Results We identified 13,286 results (excluding titles that were not relevant to this study). Then, through a narrative review, we selected 189 potential studies (189/13,286, 1.42%) from the existing literature on the adaptability of assistive mobility devices and IoT frameworks for assistive mobility and conducted a critical analysis. Of the 189 potential studies, 82 (43.4%) were selected for analysis after meeting the inclusion criteria. On the basis of the type of technologies presented in the reviewed articles, we proposed a categorization of the adaptability of smart assistive mobility devices in terms of their interaction with the user (user system interface), perception techniques, and communication and sensing frameworks. Conclusions We discussed notable limitations of the reviewed literature studies. The findings revealed that an improvement in the adaptation of assistive mobility systems would require a reduction in training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical for achieving real-world testing requirements. Finally, the trade-off between cost and performance should be considered in the commercialization of these devices.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Adaptability of Assistive Mobility Devices and the Role of the Internet of Medical Things: Comprehensive Review

Oladele¹,

Markus²,

Abu‐Mahfouz³

2021

JMIR Rehabil Assist Technol

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“…Electronic mobility cane (CVI, haptic, and auditory): liquid detection, 6 ultrasonic sensors, a metal detector, a microvibration motor, and a mono earphone [51] Has not yet been integrated and tested with assistive mobility aids to determine its applicability Intuitive control of robotic platforms by decoding as many as 20 discrete hand and finger gestures A multiclass support vector machine classifier Jet Propulsion Laboratory BioSleeve (haptic): electromyography and IMU e sensors [44,45] Fall and near-fall detection was not considered in its design and implementation.…”

Section: An Alternative Usi B For People With Spinal Cord Injury or U...mentioning

confidence: 99%

Adaptability of Assistive Mobility Devices and the Role of the Internet of Medical Things: Comprehensive Review (Preprint)

Oladele¹,

Markus²,

Abu‐Mahfouz³

2021

Preprint

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UNSTRUCTURED With the projected upsurge in the percentage of persons with some form of disability, there is a significant increase in the need for assistive mobility devices. However, these mobility aids are hardly effective without their ability to adapt to the user’s needs. This is achieved by improving the confidence of the information used or interaction between the user and his device also referred to as adaptation. In the recent past, there has been little effort to provide literature reviews on the adaptability of assistive mobility devices (AMDs). This paper systematically reviews the recent assistive mobility technologies, over the past decade, according to their adaptation and the role that the Internet of Medical Things (IoMT) has played in the adaptability of these technologies. The information gathered in the study provides awareness of the status of adaptive mobility technology and serves as a source and reference of information to healthcare professionals, and researchers. The paper starts by highlighting recent technologies according to the user system interface (human/device interface), then presents some recent technologies in perception and sensor fusion (autonomous navigation) for adaptability, and finally, IoMT frameworks for AMDs. Some notable limitations are also discussed. The findings of the review reveal that an improvement in the adaptation of assistive mobility systems would require a reduction in the training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical to achieving real-world testing requirements. Finally, the trade-off between cost and performance needs to be considered in the commercialization of these devices.

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