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Human Activity Recognition (HAR)

1.Dataset overview

This dataset is designed to support the HAR task of this study. Covered by (a) walking, (b) running, (c) going upstairs, (d) going downstairs, (e) high leg lifting, (f) skipping rope, and (g) rhombic extension Seven types of human movement data.

The files D.xlsx, E.xlsx, H.xlsx, R.xlsx, S.xlsx, U.xlsx, and W.xlsx are one-dimensional time series data of seven sports, with a data length of 400, and the number of data categories of 7.

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The dataset contains basketball activity data for nine varsity basketball players of professional skill levels. Each player wore a smart bracelet on their right wrist to record activity data during the event. The smart bracelet contains an accelerometer and gyroscope that collects acceleration and angular velocity information, and it has a sampling frequency of 50 Hz. The basketball activities of the players are laying up, passing and shooting, which are defined as shown in Table 1.

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This dataset provides Channel Impulse Response (CIR) measurements from standard-compliant IEEE 802.11ay packets to validate Integrated Sensing and Communication (ISAC) methods. The CIR sequences contain reflections of the transmitted packets on people moving in an indoor environment. They are collected with a 60 GHz software-defined radio experimentation platform based on the IEEE 802.11ay Wi-Fi standard, which is not affected by frequency offsets by operating in full-duplex mode.

The dataset is divided into three parts:

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This database includes subject-specific daily living activity data obtained from smartphones' inbuilt accelerometer, gyroscope, and linear acceleration sensors.

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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.

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Shoulder Physiotherapy Activity Recognition 9-Axis Dataset (SPARS9x) 
Suggested uses of this dataset include performing supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with unlabeled activities of daily living data.
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This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by Huawei 2 smart watches worn by healthy subjects. Subjects peform 20 repetitions of each exercise for each shoulder.

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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color

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