Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A computer system processes a digital representation of an illiquid asset, to confirm accuracy of the digital representation and value of the asset, and generates an asset-information table indicating where the various sections of the digital representation are stored. The system also generates an asset-value token, financial-return tokens, and a data structure that includes a self-executing program. The program associates the asset-value token with the financial-return tokens, so that the illiquid asset becomes a collateral for a decentralized loan transaction implemented using blockchain technology. The use of blockchain technology for decentralized loan transactions enables immutable data traceability.