Abstract: Various embodiments of a fall-detection system for detecting personal fall while preserving the privacy of a detected person are disclosed. This disclosed fall-detection system can begin by receiving a sequence of video images comprising a person being monitored. The disclosed fall-detection system then processes each video image in the sequence of video images by: detecting the person in the video image; and extracting a skeletal figure of the detected person by identifying a set of human keypoints from the detected person. Next, the disclosed fall-detection system processes the sequence of skeletal figures corresponding to the sequence of video images by labeling each skeletal figure in the sequence of skeletal figures with an action among a set of predetermined actions. The disclosed fall-detection system subsequently generates a fall/non-fall decision for the detected person based on the set of action labels corresponding to the sequence of video images.

Abstract: Various embodiments of predicting human actions are disclosed. In one aspect, a human action prediction system first receives a sequence of video images including at least a first person. Next, for each image in the sequence of video image, the human action prediction system detects the first person in the video image; and subsequently extracts a skeleton figure of the detected first person from the detected image of the first person, wherein the skeleton figure is composed of a set of human keypoints of the detected first person. Next, human action prediction system combines a sequence of extracted skeleton figures of the detected first person from the sequence of video images to form a first skeleton sequence of the detected first person which depicts a continuous motion of the detected first person.

Abstract: Various embodiments of a fall-detection system for detecting personal fall while preserving the privacy of a detected person are disclosed. This disclosed fall-detection system can begin by receiving a sequence of video images comprising a person being monitored. The disclosed fall-detection system then processes each video image in the sequence of video images by: detecting the person in the video image; and extracting a skeletal figure of the detected person by identifying a set of human keypoints from the detected person. Next, the disclosed fall-detection system processes the sequence of skeletal figures corresponding to the sequence of video images by labeling each skeletal figure in the sequence of skeletal figures with an action among a set of predetermined actions. The disclosed fall-detection system subsequently generates a fall/non-fall decision for the detected person based on the set of action labels corresponding to the sequence of video images.

Abstract: Various embodiments of a health-monitoring system are disclosed. In one aspect, this health-monitoring system includes a server and a set of health-monitoring sensors communicatively coupled to the server. In some embodiments, the server is configured to establish a new profile for a person to be monitored by: receiving a new profile request for establishing the new profile; generating a unique person-ID for the received new profile request; creating a new entry including the unique person-ID for the person in a profile database; and transmitting the unique person-ID along with the profile photos of the person to the set of health-monitoring sensors. Moreover, each health-monitoring sensor in the set of sensors is configured to establish the new profile by adding a new entry for the person in a person-ID dictionary of the health-monitoring sensor based on the received person-ID and the profile photos of the person.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.