Abstract: According to one aspect, action prediction may be implemented via a spatio-temporal feature pyramid graph convolutional network (ST-FP-GCN) including a first pyramid layer, a second pyramid layer, a third pyramid layer, etc. The first pyramid layer may include a first graph convolution network (GCN), a fusion gate, and a first long-short-term-memory (LSTM) gate. The second pyramid layer may include a first convolution operator, a first summation operator, a first mask pool operator, a second GCN, a first upsampling operator, and a second LSTM gate. An output summation operator may sum a first LSTM output and a second LSTM output to generate an output indicative of an action prediction for an inputted image sequence and an inputted pose sequence.

Abstract: In some embodiments, a system is provided that comprises a computing system including at least one computing device and a camera communicatively coupled to the computing system via a network. The computing system is configured to generate a set of features based on three-dimensional joint location data representing postures of a subject over a plurality of time steps depicted in video data captured by the camera; provide the set of features to a first machine learning model trained to identify a start time step, an end time step, and an action identity for each action; provide the set of features to a second machine learning model trained to determine a postural assessment score for each time step; and determine an action score for each action based on the start time steps, the end time steps, the action identities, and the postural assessment scores for each time step.

Abstract: According to one aspect, action prediction may be implemented via a spatio-temporal feature pyramid graph convolutional network (ST-FP-GCN) including a first pyramid layer, a second pyramid layer, a third pyramid layer, etc. The first pyramid layer may include a first graph convolution network (GCN), a fusion gate, and a first long-short-term-memory (LSTM) gate. The second pyramid layer may include a first convolution operator, a first summation operator, a first mask pool operator, a second GCN, a first upsampling operator, and a second LSTM gate. An output summation operator may sum a first LSTM output and a second LSTM output to generate an output indicative of an action prediction for an inputted image sequence and an inputted pose sequence.