Wearable Sensing
The following database contains over 25,000 records collected using the author's data glove while researching 16 static letters of the Polish Sign Language alphabet.
Data are readings from 10 piezoresistive sensors placed over the fingers of the hand expressed in ADC values.
Data are from 15 test subjects, each of whom performed each letter ten times
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The accelerometer data has been collected using one smartphone carried by subjects, which are caregivers and nurses, when they were conducting daily works at a healthcare facility. The smartphone was carried in an arbitrary position such as a pocket. There are a total of 27 activities divided into 4 groups.
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120 Views
The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
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1000 Views
This is the data for the paper "Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode" published on IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.
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253 Views
We collect IMU measurements under three different patterns: Fixing a smartphone in front of his chest (chest), swing a smartphone while holding it in his hand (swing), and putting a smartphone in his pocket (pocket). We use Google Pixel 3XL for the pattern of chest and Google Pixel 3a for the patterns of swing and pocket. The sampling frequency of each measurement is fixed to 15Hz. We collect the measurement of 111 paths in total, categorized into 4 types. We partition them into 84 and 27 paths, used for training and testing, respectively. It takes 10 hours to collect all datasets.
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204 Views
Visual systems can improve transitions between different locomotion mode controllers (e.g., level-ground walking to stair ascent) by sensing the walking environment prior to physical interactions. Here we developed StairNet to support the development of vision-based stair recognition systems for robotic leg control. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.
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2726 Views
Context awareness is an emerging field in pervasive computing with applications that have started to emerge in medical systems. The present work seeks to determine which contexts are important for medical applications and what various domains of context aware applications exist in healthcare. Methods: A systematic scoping review of context aware medical systems currently being used in healthcare settings was conducted.
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326 Views
This dataset is related to dog activity and is sensor data.
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691 Views
There exist several commonly used datasets in relation to object detection that include COCO (with multiple versions) and ImageNet containing large annotations for 80 and 1000 objects (i.e. classes) respectively. However, very limited datasets are available comprising specific objects identified by visually imapeired people (VIP) such as wheel-bins, trash-Bags, e-Scooters, advertising boards, and bollard. Furthermore, the annotations for these objects are not available in existing sources.
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204 Views