Wearable Sensing

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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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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The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).

All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.

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The dataset involves two sets of participants: a group of twenty skilled drivers aged between 40 and 68, each having a minimum of ten years of driving experience (class 1), and another group consisting of ten novice drivers aged between 18 and 46, who were currently undergoing driving lessons at a driving school (class 2).

The data was recorded using JINS MEME ES_R smart glasses by JINS, Inc. (Tokyo, Japan).

Each file consists of a signals from one sigle ride.

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The dataset Arabic Digit Sequential Electromyography (ADSE) is acquired for eight-lead sEMG data targeting sequential signals.

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Highly accurate and lightweight automated movements quality assessment is essential for home rehabilitation patients. We propose a method for the assessment and quantification of movement quality based on the differential feature segments, the objective  is to emulate the expert evaluations of physicians as closely as possible with minimal data features. Employing the Gaussian mixture model (GMM) to divide continuous trend time-series data into fragment features, defined as feature segments.

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Autistic people typically need methodical support as they explore and interact with their immediate surroundings and the objects associated with them, emphasising the importance of spatial knowledge and cognitive skills in improving and understanding their surroundings. The objective of this research paper is to present a conceptual and technical framework that could be of significant assistance in developing spatial ability and cognitive skills in autistic people.

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The performance, repeatability, and stability of the sensor were measured,Sensors are applied to gesture recognition and human-computer interaction. By utilizing wavelength division multiplexing technology, multiple sensors measure finger movements and improve recognition accuracy through backpropagation neural network algorithms. Five commonly used gestures were recognized and the sensor posture was determined based on light intensity.

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<p><span style="color: #3c4043; font-family: Inter, sans-serif; font-size: 14px;">The dataset is collected from 3 MPU9250 sensors connected simultaneously on different positions of the hand. One sensor was placed on the wrist, another between wrist and elbow and another between elbow and shoulder. The dataset contains a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer readings along with a result column in which '1' denoted shaking hand and '0' denoted stable hand.

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A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.

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