View source for 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==

[[:Category:Apple Inc.|Apple Inc.]]


[[Category:Apple Inc.]]

==Inventor(s)==

[[:Category:Aditya Sarathy of Santa Clara CA (US)|Aditya Sarathy of Santa Clara CA (US)]][[Category:Aditya Sarathy of Santa Clara CA (US)]]

[[:Category:Xiaoyuan Tu of Sunnyvale CA (US)|Xiaoyuan Tu of Sunnyvale CA (US)]][[Category:Xiaoyuan Tu of Sunnyvale CA (US)]]

[[:Category:Suresh B. Malakar of Cupertino CA (US)|Suresh B. Malakar of Cupertino CA (US)]][[Category:Suresh B. Malakar of Cupertino CA (US)]]

[[:Category:Hui Lin of Sunnyvale CA (US)|Hui Lin of Sunnyvale CA (US)]][[Category:Hui Lin of Sunnyvale 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.



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