Wearable Sensing

These are some graphs that record the human ocular electrical signals and ocular impedance signals, each image from top to bottom is a time-frequency graph of the EOG, the EOG signals, the time-frequency graph of the impedance signals, the impedance signals, and the impedance signals, respectively. This dataset is used to train the eye movement detection model.

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These are some graphs that record the human ocular electrical signals and ocular impedance signals, each image from top to bottom is a time-frequency graph of the EOG, the EOG signals, the time-frequency graph of the impedance signals, the impedance signals, and the impedance signals, respectively. This dataset is used to train the eye movement detection model.

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An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.

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The dataset contains data obtained by measuring hand movements while performing the letters of the Polish Sign Language alphabet. It contains data from 16 users performing all 36 letters ten times. Each single execution of a gesture is recorded in 75 samples. The experiment also included data augmentation, multiplying the number of data by 200. times.

 

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Wearable devices, such as data gloves and electronic skins, can perceive human hand's actions, behaviors and even emotions with the help of knowledge learning and inference. Curvature or magnetism sensing in such devices often lacks comprehensive gesture interactive information, meanwhile, the limited computing power of wearable applications restricts the multi-mode fusion of different sensing data and the deployment of deep learning networks.

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The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.

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We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.

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Human-robot interaction remains a significant challenge for mobile robots performing as social robots, which are expected to coexist, collaborate, and be cognitive with persons to enhance efficiency and reduce labor strength. Typically, escort robots can be widely employed for accompanying elderly and youth outdoors, guiding visitors in exhibition halls and museums, and sorting goods in warehouses.

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The poor posture is one of the main common health problems in the growth of adolescents, which seriously affects their physical and mental health. The posture gait recognition is a premise for preventing and correcting the poor posture. This paper proposes a gait recognition method for poor posture based on PCA-BP neural network. Using wearable intelligent insoles to measure plantar pressure, a gait recognition model based on PCA-BP neural network model is constructed.

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Wearable and low power devices are vulnerable to side-channel attacks, which can retrieve private data (like sensitive data or the private key of a cryptographic algorithm) based on externally measured magnitudes, like power consumption. These attacks have a high dependence on the data being encrypted -- the more variable it is, the more information an attacker will have for performing it. This database contains ECG data measured with a wearable sensorized garment during different levels of activity.

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