Abstract: Systems and methods of determining a mis-step of a user with gait abnormality, including: calibrating signals corresponding to motion by the user in a controlled environment to identify the gait of the user, detecting a movement of the user to determine a change from the determined gait, detecting at least one physiological signal of the user, identifying motion carried out by the user based on data from at least one wearable sensor, and determining at least one mis-step carried out by the user using a machine learning algorithm trained to determine steps based on the identified motion.

Abstract: A method for monitoring heating, ventilation, and air conditioning (HVAC) systems includes: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC consumption or generating additional derived parameters, in an aggregation process; Calculating the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of summer weather conditions; Obtaining operational data pertaining to HVACs in an operational set of households during a second period of spring weather; and Applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set, during periods of summer weather using only overall

Abstract: The present invention provides a method for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the following steps: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data for HVACs in the training set of households during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC coemption or generating additional derived parameters, in an aggregation process Calculating a “Household Efficiency Score”: the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an