Abstract: A wearable device measures, tracks, and monitors a wearer's physical physiological conditions during a rehabilitation period. Metrics such as temperature, patellar shifting, limb circumference, and acceleration may be collected. Changes in monitored conditions may be assessed for detecting medical abnormalities which require specialized attention, including for example embolisms or infections. A networked communication system and various user interfaces may be used for medical support personnel and patients alike to stay updated with the patient's rehabilitation progress and to make adjustments in a patient's individual rehabilitation program.

Abstract: A wearable device measures, tracks, and monitors a wearer's physical physiological conditions during a rehabilitation period. Metrics such as temperature, patellar shifting, limb circumference, and acceleration may be collected. Changes in monitored conditions may be assessed for detecting medical abnormalities which require specialized attention, including for example embolisms or infections. A networked communication system and various user interfaces may be used for medical support personnel and patients alike to stay updated with the patient's rehabilitation progress and to make adjustments in a patient's individual rehabilitation program.

Abstract: A wearable device measures, tracks, and monitors a wearer's physical physiological conditions during a rehabilitation period. Metrics such as temperature, patellar shifting, limb circumference, and acceleration may be collected. Changes in monitored conditions may be assessed for detecting medical abnormalities which require specialized attention, including for example embolisms or infections. A networked communication system and various user interfaces may be used for medical support personnel and patients alike to stay updated with the patient's rehabilitation progress and to make adjustments in a patient's individual rehabilitation program.

Abstract: A knowledge acquisition system and artificial cognitive declarative memory model to store and retrieve massive student learning datasets. The memory storage model enables storage and retrieval of massive data derived using multiple interleaved machine-learning artificial intelligence models to parse, tag, and index academic, communication, and social student data cohorts as applied to academic achievement. Artificial Episodic Recall Promoters assist recall of academic subject matter for knowledge acquisition. A Deep Academic Learning Intelligence system for machine learning-based student services provides monitoring and aggregating performance information and student communications data in an online group learning course. The system uses communication activity, social activity, and the academic achievement data to present a set of recommendations and uses responses and post-recommendation data as feedback to further train the machine learning-based system.