# US 10,318,890 B1 · Training data for a motion detection system using data from a sensor device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,318,890 B1 - Document ID: US10318890B1 - Status: Granted - Kind: B1 - Application number: 15/987,805 - Filed: 2018-05-23 - Granted: 2019-06-11 - Earliest claimed: 2018-05-23 - Assignees: Cognitive Systems Corp. - Applicants: Cognitive Systems Corp. - Inventors: Oleksiy Kravets; Mikhail Ravkine - Web page: https://patentgrep.com/patent/US10318890B1 ## CPC - G06N 20/00: Machine learning - G01S 7/415: {Identification of targets based on measurements of movement associated with the target} - G01S 13/886: {for alarm systems (alarms with electrical actuation G08B13/22)} - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - H04L 25/0224: {using sounding signals} - H04W 4/029: Location-based management or tracking services - H04W 4/38: for collecting sensor information - H04W 24/08: Testing, {supervising or monitoring} using real traffic - G01S 13/003: {Bistatic radar systems; Multistatic radar systems} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract In a general aspect of the examples described, sensor data from a sensor device are used to improve training data for a motion detection system. In some aspects, “ground truth” labels, sensor data, and channel information are received for a motion detection training period. The labels and sensor data are analyzed to detect variances between the labels and sensor data. The labels are corrected or augmented based on the sensor data. The channel information is tagged with the labels and provided as training information to train the motion detection system. ## Claims ### Claim 1 A motion detection training method comprising: receiving, by one or more processors of a data processing apparatus, channel information based on training signals, the training signals comprising wireless signals transmitted by a wireless communication device through a space between the wireless communication device and a second wireless communication device during a motion detection training time period supervised by a user; receiving, by the one or more processors of the data processing apparatus, a label comprising a first set of motion information input to a user interface by the user of a mobile device in the space during the motion detection training time period; receiving, by the one or more processors of the data processing apparatus, sensor data comprising a second set of motion information collected by a wearable sensor device of the user in the space during the motion detection training time period; analyzing, by the one or more processors of the data processing apparatus, the label and the sensor data to detect a variance between the label and the sensor data for the motion detection training time period, wherein analyzing the label and the sensor data comprises: detecting the variance when the analysis indicates that the sensor data does not correspond to the label; and either correcting the label based on the received sensor data, or augmenting the label with the received sensor data, depending on the variance detected; generating, by the one or more processors of the data processing apparatus, a tag for the channel information based on the analysis of the label and the sensor data; and providing, by the one or more processors of the data processing apparatus, the channel information and the tag as training input to train a motion detection system. ### Claim 2 (depends on 1) The motion detection training method of claim 1, wherein generating the tag based on the analysis of the label and the sensor data improves accuracy of the tag compared to generating the tag only from the label. ### Claim 3 (depends on 1) The motion detection training method of claim 1, wherein the sensor device comprises at least one of: an accelerometer; a gyroscope; a compass; a pedometer; and a barometer. ### Claim 4 (depends on 1) The motion detection training method of claim 1, wherein the mobile device comprises the sensor device. ### Claim 5 (depends on 1) The motion detection training method of claim 1, wherein the variance between the label and the sensor data comprises at least one of: a difference between a timestamp in the label and a timestamp in the sensor data; a difference between a type of measurement indicated in the label and a type of measurement in the sensor data; or additional information provided in the sensor data but not the label. ### Claim 6 (depends on 1) The motion detection training method of claim 1, wherein providing the channel information and the tag as training input to train the motion detection system comprises providing a tagged channel information to a machine learning system. ### Claim 7 (depends on 6) The motion detection training method of claim 6, wherein the tag indicates at least one of: whether motion occurred in the space during the motion detection training time period; a direction of motion that occurred in the space during the motion detection training time period; a location of motion that occurred in the space during the motion detecting training time period; or a time at which motion occurred in the space during the motion detection training time period. ### Claim 8 A motion detection training system comprising: one or more processors; memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: receive channel information based on training signals, the training signals comprising wireless signals transmitted by a wireless communication device to a second wireless communication device through a space during a motion detection training time period supervised by a user; receive a label comprising a first set of motion information input to a user interface by the user of a mobile device in the space during the motion detection training time period; receive sensor data comprising a second set of motion information collected by a wearable sensor device of the user in the space during the motion detection training time period; wherein analyzing the label and the sensor data comprises: detecting the variance when the analysis indicates that the sensor data does not correspond to the label; and either correcting the label based on the received sensor data, or augmenting the label with the received sensor data, depending on the variance detected; analyze the label and the sensor data to detect a variance between the label and the sensor data for the motion detection training time period; generate a tag for the channel information based on the analysis of the label and the sensor data; and provide the channel information and the tag as training input to train a motion detection system. ### Claim 9 (depends on 8) The motion detection training system of claim 8, wherein generating the tag based on the analysis of the label and the sensor data improves accuracy of the tag compared to generating the tag only from the label. ### Claim 10 (depends on 8) The motion detection training system of claim 8, wherein the sensor device comprises at least one of: an accelerometer; a gyroscope; a compass; a pedometer; and a barometer. ### Claim 11 (depends on 8) The motion detection training system of claim 8, wherein the mobile device comprises the sensor device. ### Claim 12 (depends on 8) The motion detection training system of claim 8, wherein the variance between the label and the sensor data comprises at least one of: a difference between a timestamp in the label and a timestamp in the sensor data; a difference between a type of measurement indicated in the label and a type of measurement in the sensor data; or additional information provided in the sensor data but not the label. ### Claim 13 (depends on 8) The motion detection training system of claim 8, wherein providing the channel information and the tag as training input to train the motion detection system comprises providing a tagged channel information to a machine learning system. ### Claim 14 (depends on 13) The motion detection training system of claim 13, wherein the tag indicates at least one of: whether motion occurred in the space during the motion detection training time period; a direction of motion that occurred in the space during the motion detection training time period; a location of motion that occurred in the space during the motion detection training time period; or a time at which motion occurred in the space during the motion detection training time period. ### Claim 15 A non-transitory computer readable medium comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: receiving channel information based on training signals, the training signals comprising wireless signals transmitted by a wireless communication device to a second wireless communication device through a space during a motion detection training time period supervised by a user; receiving a label comprising a first set of motion information input to a user interface by the user of a mobile device in the space during the motion detection training time period; receiving sensor data comprising a second set of motion information collected by a wearable sensor device of the user in the space during the motion detection training time period; analyzing the label and the sensor data to detect a variance between the label and the sensor data for the motion detection training time period, wherein analyzing the label and the sensor data comprises: detecting the variance when the analysis indicates that the sensor data does not correspond to the label; and either correcting the label based on the received sensor data, or augmenting the label with the received sensor data, depending on the variance detected; generating a tag for the channel information based on the analysis of the label and the sensor data; and providing the channel information and the tag as training input to train a motion detection system. ### Claim 16 (depends on 15) The computer readable medium of claim 15, wherein generating the tag based on the analysis of the label and the sensor data improves accuracy of the tag compared to generating the tag only from the label. ### Claim 17 (depends on 15) The computer readable medium of claim 15, wherein the sensor device comprises at least one of: an accelerometer; a gyroscope; a compass; a pedometer; and a barometer. ### Claim 18 (depends on 15) The computer readable medium of claim 15, wherein the mobile device comprises the sensor device. ### Claim 19 (depends on 15) The computer readable medium of claim 15, wherein the variance between the label and the sensor data comprises at least one of: a difference between a timestamp in the label and a timestamp in the sensor data; a difference between a type of measurement indicated in the label and a type of measurement in the sensor data; or additional information provided in the sensor data but not the label. ### Claim 20 (depends on 15) The computer readable medium of claim 15, wherein providing the channel information and the tag as training input to train the motion detection system comprises providing a tagged channel information to a machine learning system. ### Claim 21 (depends on 20) The computer readable medium of claim 20, wherein the tag indicates at least one of: whether motion occurred in the space during the motion detection training time period; a direction of motion that occurred in the space during the motion detection training time period; a location of motion that occurred in the space during the motion detection training time period; or a time at which motion occurred in the space during the motion detection training time period.