guillaume-chevalier/LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
This project helps anyone working with sensor data from smartphones to automatically identify six common human activities: walking, walking upstairs, walking downstairs, sitting, standing, and laying. It takes raw accelerometer and gyroscope data as input and outputs a classification of the activity being performed. This is useful for researchers, product developers, or data analysts in fields like health, fitness, or behavioral science.
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Use this if you need to classify human activities from smartphone sensor data without extensive manual feature engineering.
Not ideal if your activity recognition needs involve different sensor types, very high-frequency movements, or a broader range of complex activities beyond the six defined categories.
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Nov 06, 2022
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