# US 11,992,335 B2 · System for health monitoring on prosthetic and fixation devices

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- Number: US 11,992,335 B2
- Document ID: US11992335B2
- Status: Granted
- Kind: B2
- Application number: 18/351,409
- Filed: 2023-07-12
- Published: 2023-11-09
- Granted: 2024-05-28
- Earliest claimed: 2013-03-07
- Assignees: The Regents of the University of California
- Applicants: The Regents of the University of California
- Inventors: Fabrizio Billi; Per Henrik Borgstrom; William Kaiser; Harry A. McKellop
- Web page: https://patentgrep.com/patent/US11992335B2

## CPC

- A61B 5/4851: {Prosthesis assessment or monitoring}
- A61B 5/1121: {Determining geometric values, e.g. centre of rotation or angular range of movement}
- A61B 5/1122: {of movement trajectories}
- A61B 5/4528: {Joints (A61B5/4533, A61B5/4538 take precedence)}
- A61B 5/6812: {Orthopaedic devices}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 7/006: {Detecting skeletal, cartilage or muscle noise (A61B7/003 and A61B7/008 take precedence)}
- G16H 40/63: for local operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 5/4514: {Cartilage}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 2560/0475: Special features of memory means, e.g. removable memory cards
- A61B 2562/0204: Acoustic sensors
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

## Claims

### Claim 1

A non-transitory computer-readable medium comprising computer-executable instructions to:
access time-stamped digital acoustic data and time-stamped digital motion data;
detect a sample event buffer set from the digital acoustic data;
calculate angle information from the digital motion data;
determine an angle-resolved event from the detected sample event buffer set and calculated angle information;
calculate in the time domain an event envelope from the sample event buffer set by taking the square root of the sum of squares of the sample event buffer set and the respective Hilbert Transforms of the sample event buffer set in the time domain;
determine a plurality of vector elements that form a feature vector in the time domain that measures an amplitude of the event envelope in the time domain;
calculate a Euclidean distance between the feature vector in the time domain and each of a set of predetermined event silhouettes; and
identify one of the predetermined event silhouettes for which the Euclidean distance is a minimum.

### Claim 2 (depends on 1)

The non-transitory computer-readable medium of claim 1, wherein the angle-resolved event is determined based in part on time stamps of the digital acoustic data and the digital motion data.

### Claim 3 (depends on 1)

The non-transitory computer-readable medium of claim 1, wherein the predetermined event silhouette is one of a set of predetermined event silhouettes related to the angle-resolved event, and each of the set of predetermined event silhouettes is determined in part using unbiased, hierarchical clustering.

### Claim 4 (depends on 1)

The non-transitory computer-readable medium of claim 1, further including instructions to select an event type based on the angle-resolved event.

### Claim 5 (depends on 1)

The non-transitory computer-readable medium of claim 1, further including instructions to identify a source of acoustic emissions in a human body from the identification of the predetermined event silhouette.

### Claim 6 (depends on 5)

The non-transitory computer-readable medium of claim 5, further including instructions to determine a health status of a portion of the human body from the event envelope.