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1.0.1

PyPI

adl-recognition

Classification of Activities of Daily Living(ADL) using depth videos and audio

Published25 Dec 2019

Direct dependencies5

Transitive dependenciesNot calculated

README

Version 1.0.1 documentation

AssistedLivingSystem

Recognition of Activities of Daily Living(ADL) with privacy protection using depth and audio data.

Activities

Making a phone call, clapping, drinking, eating, entering from door, exiting from door, falling, lying down, opening pill container, picking object, reading, sit still, sitting down, sleeping, standing up, sweeping, using laptop, using phone, wake up, walking, washing hand, watching TV, water pouring and writing

Dependencies

python >= 2.7
numpy
librosa
noisereduce
cv2
keras

This project is developed as a FYP, UoM, 2019.

License

MIT

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Declared dependencies 5

Native requirements

5 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
numpy Environment { "extras": "", "marker": null, "requires_python": ">=2.7", "evaluation": "NOT_EVALUATED" }	`numpy (>=1.17.2)`	runtime
librosa Environment { "extras": "", "marker": null, "requires_python": ">=2.7", "evaluation": "NOT_EVALUATED" }	`librosa (>=0.6.0)`	runtime
noisereduce Environment { "extras": "", "marker": null, "requires_python": ">=2.7", "evaluation": "NOT_EVALUATED" }	`noisereduce (>=1.0.0)`	runtime
opencv-python Environment { "extras": "", "marker": null, "requires_python": ">=2.7", "evaluation": "NOT_EVALUATED" }	`opencv-python (>=4.1.1.26)`	runtime
keras Environment { "extras": "", "marker": null, "requires_python": ">=2.7", "evaluation": "NOT_EVALUATED" }	`keras (>=2.2.0)`	runtime