Wearable Sensing
The image displays four segments of gestures from our dataset.
(a) The video sequence of rotating the wrist down and up as a signal for starting a new gesture.
(b)–(d) Three gestures samples (the triangle, letter b, and letter Z) taken from three different subjects at three different scenes (sitting at a desk, standing indoors, and standing outdoors.). The trajectory of each gesture canbe recognized from the movement of the background objects.
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In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier.
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These .s2p files contain the S-parameters measured between two on-neck antennas for multiple test subjects acting out four activites. Each files is one trial of measurement, containing 20 seconds of data sampled at 200 Hz.
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The file contains raw data collected from 9 pedestrians. Three of them walked in Track 1, another three walked in Track 2 and the last three walked in Track 3. All the pedestrians ended their walks at the starting point. Track 1 and Track 3 cover a distance of 150.3m. While, the Track covers a distance of 111.4m.
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The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.
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749 Views
An energy harvester for a smart contact lens that monitors the glucose level of a user is developed and demonstrated. The energy harvester captures a smartphone’s 2G cellular emission, and rectifies it into DC power to operate on-lens microelectronics for glucose detection and wireless data transmission. The energy harvester can reach a maximum Ra- dio Frequency (RF) to Direct Current (DC) power conversion efficiency of 47%. An electrically realistic human eye model is designed and fabricated using 3D printing technologies to assist in various measurements of the proposed energy harvester.
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387 Views
The dataset is an extensive collection of labeled high-frequency Wi-Fi Radio Signal Strength (RSS) measurements corresponding to multiple hand gestures made near a smartphone under different spatial and data traffic scenarios. We open source the software code and an Android app (Winiff) to create this dataset, which is available at Github (https://github.com/mohaseeb/wisture). The dataset is created using an artificial traffic induction (between the phone and the access point) approach to enable useful and meaningful RSS value
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1179 Views
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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies. Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.
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4047 Views
Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. We have used a smart watch (Apple iWatch) to collect sensory data for 14 ADL activities (Activities of Daily Living).
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1625 Views