June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Data collection
Data collection for HAR and/FER is a challenging task for many reasons, including privacy. As a result, open source quality datasets are limited in number. For the HAR implementation in use case one, we are using a very popular and open source Wireless Sensor Data Mining (WISDM) lab dataset . The dataset consists of 54,901 samples collected from 36 different subjects. For privacy reasons, usernames are masked with ID numbers from 1-36. The data was collected for six different activities undertaken by the subjects: standing, sitting, jogging, walking, going downstairs, and climbing upstairs. The dataset contains three-axis accelerometer data with more than 200 time steps for each sample. The following screenshot is a sample ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.