WearIT@work: Toward Real-World Industrial Wearable Computing

Lukowicz, Paul; Timm‐Giel, Andreas; Lawo, Michael; Herzog, Otthein

doi:10.1109/mprv.2007.89

Cited by 70 publications

(25 citation statements)

References 5 publications

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“…A European Community Project called WearIT@work 30 satisfies the "comfort" requirement by encouraging physicians to use a wrist-mounted RFID reader to identify the patient and interact through gestures with the hospital information system to document exams and write prescriptions, helping ensure sterility. However, since this is an encumbered interface, the "come as you are" requirement is violated.…”

Section: Applicationsmentioning

confidence: 99%

Vision-based hand-gesture applications

et al. 2011

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Section: Applicationsmentioning

confidence: 99%

Vision-based hand-gesture applications

et al. 2011

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“…"Gestix" addressed both the "come as you are" and "intuitiveness" requirements by providing a natural effective interface. The "comfort" requirement is fulfilled in "WearIT@work" [26], a RFID reader is used to identify the patient and to interact with the hospital information system (HIS) using gestures to fill out exams and prescriptions. This project ensures sterility.…”

Section: Hand Gesture Recognition In Healthcarementioning

confidence: 99%

Gaze, Posture and Gesture Recognition to Minimize Focus Shifts for Intelligent Operating Rooms in a Collaborative Support System

Wachs

2010

INT J COMPUT COMMUN

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This paper describes the design of intelligent, collaborative operating rooms based on highly intuitive, natural and multimodal interaction. Intelligent operating rooms minimize surgeon's focus shifts by minimizing both the focus spatial offset (distance moved by surgeon's head or gaze to the new target) and the movement spatial offset (distance surgeon covers physically). These spatio-temporal measures have an impact on the surgeon's performance in the operating room. I describe how machine vision techniques are used to extract spatio-temporal measures and to interact with the system, and how computer graphics techniques can be used to display visual medical information effectively and rapidly. Design considerations are discussed and examples showing the feasibility of the different approaches are presented.

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“…The dataset was recorded within the scope of an industrial project focusing on wearable technology in production environments [23]. In total, 12 h of data were recorded.…”

Section: B Car Quality Inspectionmentioning

confidence: 99%

All for one or one for all? Combining heterogeneous features for activity spotting

Blanke

Schiele

Kreil

et al. 2010

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.

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WearIT@work: Toward Real-World Industrial Wearable Computing

Cited by 70 publications

References 5 publications

Vision-based hand-gesture applications

Vision-based hand-gesture applications

Gaze, Posture and Gesture Recognition to Minimize Focus Shifts for Intelligent Operating Rooms in a Collaborative Support System

All for one or one for all? Combining heterogeneous features for activity spotting

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