Michael L Black

US Patent:

20040073414, Apr 15, 2004

Filed:

Jun 4, 2003

Appl. No.:

10/455509

Inventors:

Lucien Bienenstock - Providence RI, US
Michael Black - Providence RI, US
Wei Wu - Providence RI, US
Yun Gao - Providence RI, US

Assignee:

Brown University Research Foundation

International Classification:

G06F017/10

US Classification:

703/002000

Abstract:

A method and system to decode neural activity in the motor cortex to infer at least the position and velocity of a subject's hand from neural spiking activity of some number of nerve cells. In one embodiment the method includes simultaneously recording electrical activity of the nerve cells in the primary motor cortex to obtain neural data; and modeling the encoding and decoding of the neural data using a Kalman filter, where a measurement model assumes a cell firing rate to be a stochastic linear function of at least the position and velocity of the hand, and where the measurement model is learned from training data in conjunction with a system model that encodes a manner in which the hand moves. In another embodiment the method includes using the neural data to generate training data of neural firing activity conditioned on hand kinematics; learning a non-parametric representation of the firing activity using a Bayesian model; inferring an a posterior probability distribution over hand motion, conditioned on the neural training data using Bayesian inference; defining a non-Gaussian likelihood term that is combined with a prior probability for the kinematics based on learned firing models of multiple nerve cells; and using a particle filtering method is to represent, update, and propagate the posterior distribution over time.

US Patent:

20060165811, Jul 27, 2006

Filed:

Mar 22, 2005

Appl. No.:

11/086956

Inventors:

Michael Black - Providence RI, US
Frank Wood - Providence RI, US
Wei Wu - Chicago IL, US

International Classification:

A61K 35/30

US Classification:

424570000

Abstract:

A Switching Kalman Filter Model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A “hidden state” models the probability of each mixture component and evolves over time in a Markov chain. Gaussian mixture models and Expectation Maximization (EM) techniques are extended for automatic spike sorting. Good initialization of EM is achieved via spectral clustering. To account for noise, the mixture model is extended to include a uniform outlier process. A greedy optimization algorithm that selects models with different numbers of neurons according to their decoding accuracy is used to automatically determine the number of neurons recorded per electrode. Closed loop neural control of external events are demonstrated using neural control of a computer curser.

US Patent:

20100111370, May 6, 2010

Filed:

Aug 14, 2009

Appl. No.:

12/541898

Inventors:

Michael J. Black - Providence RI, US
Alexandru O. Balan - Pawtucket RI, US
Alexander W. Weiss - Shirley MA, US
Leonid Sigal - Pittsburgh PA, US
Matthew M. Loper - Providence RI, US
Timothy S. St. Clair - Cambridge MA, US

International Classification:

G06K 9/00
G06Q 30/00
G06Q 50/00
H04N 7/18
G06K 9/34
G06K 9/68

US Classification:

382111, 705 27, 348 77, 382173, 382164, 382218, 348E07085

Abstract:

A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.