Activity recognition model balanced between versatility and individuation and system thereof

An activity recognition method balanced between versatility and individuation, being characterized in at least comprising one or more steps of:

• using pre-processing techniques including at least one or more of calibration, noise reduction and interpolation to process a received sensing signal, and according to characteristics of a data source conducting signal sharding, dimensionality compression, and characteristic extraction, so as to obtain a structured data;
• using the structured data as an input of a recognition model, using an activity recognition model to recognize a personnel activity, and continuously verifying user IDs;
• where it is determined that a current user has not been registered, downloading a pre-trained model from a cloud computing platform to a current edge computing device;
• where it is determined that the current user is a new user, training the user identifier and fine-tuning the activity recognizer according to on a small amount of training data provided by the user;
• where it is determined that the current user is a registered user, the pre-trained model collecting data of the user at a back end for on-line learning, and generating local model updating parameters;
• uploading the local model updating parameters to the cloud computing platform;
• and
• when an updating condition for a general model is satisfied, the cloud computing platform integrating the model updating parameters it receives from all the edge computing devices for updating the versatile model.