17485212. POSTURE TRANSITION DETECTION AND CLASSIFICATION USING LINKED BIOMECHANICAL MODEL simplified abstract (Apple Inc.)
POSTURE TRANSITION DETECTION AND CLASSIFICATION USING LINKED BIOMECHANICAL MODEL
Organization Name
Inventor(s)
Aditya Sarathy of Santa Clara CA (US)
Xiaoyuan Tu of Sunnyvale CA (US)
Suresh B. Malakar of Cupertino CA (US)
POSTURE TRANSITION DETECTION AND CLASSIFICATION USING LINKED BIOMECHANICAL MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 17485212 titled 'POSTURE TRANSITION DETECTION AND CLASSIFICATION USING LINKED BIOMECHANICAL MODEL
Simplified Explanation
The patent application describes a method for detecting and classifying user posture transitions using a linked biomechanical model. Here is a simplified explanation of the abstract:
- The method involves obtaining motion data from a headset worn by a user.
- Features of a linked biomechanical model are selected based on the user's current posture state.
- A classifier is used to determine the probability of a posture transition based on the selected features and motion data.
- The method then identifies a posture transition based on the calculated probabilities.
- Finally, actions are performed based on the detection of the posture transition.
Potential applications of this technology:
- Virtual reality and augmented reality systems can use this method to detect and classify user posture transitions, allowing for more immersive and interactive experiences.
- Fitness and healthcare applications can utilize this technology to monitor and analyze user movements, providing feedback and guidance for proper posture and exercise techniques.
- Ergonomics and workplace safety can benefit from this method by detecting and alerting users to incorrect or potentially harmful postures.
Problems solved by this technology:
- Traditional methods of detecting posture transitions may be limited in accuracy and reliability. This method improves upon existing techniques by using a linked biomechanical model and motion data from a headset to enhance detection and classification.
- By accurately detecting and classifying posture transitions, this technology can help prevent injuries and improve user comfort and performance in various applications.
Benefits of this technology:
- The use of a linked biomechanical model and motion data allows for more precise and real-time detection of posture transitions.
- The method can adapt to different user postures and movements, making it versatile and applicable to a wide range of scenarios.
- By performing actions based on detected posture transitions, this technology can enhance user experiences and improve overall safety and well-being.
Original Abstract Submitted
Embodiments are disclosed for user posture transition detection and classification using a linked biomechanical model. In an embodiment, a method comprises: obtaining motion data from a headset worn by a user; selecting features of a linked biomechanical model based on a current posture state; determining at least one probability that a posture transition occurred based on an output of a classifier, where the output of the classifier is based on the selected features and the motion data; determining a posture transition based on the at least one probability; and performing at least one action based on detection of the posture transition.
