# US 2013/0158686 A1 · Intelligent activity monitor

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- Number: US 2013/0158686 A1
- Document ID: US20130158686A1
- Status: Published application
- Kind: A1
- Application number: 13/690,313
- Filed: 2012-11-30
- Published: 2013-06-20
- Earliest claimed: 2011-12-02
- Assignees: FitLinxx, Inc.
- Inventors: Jerry Zhang; Paul J. Gaudet; Norbert Ohlenbusch; David P. Monahan; Thomas P. Blackadar; Thomas Quinlan
- Web page: https://patentgrep.com/patent/US20130158686A1

## CPC

- A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance}
- A61B 5/1118: {Determining activity level}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- G01C 22/006: {Pedometers}
- A61B 5/7239: {using differentiation including higher order derivatives}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 2560/0209: adapted for power saving
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)

## Abstract

Apparatuses and methods are disclosed for identifying with a single, small activity monitor a particular type of activity from among a plurality of different activities. The monitor may include a multi-axis accelerometer and microcontroller configured to combine and process accelerometer data so as to generate features representative of an activity. The features may be processed to identify a particular activity (e.g., running, biking, swimming) from among a plurality of different activities that may include activities not performed by a human subject. The activity monitor may be configured to further process the features to calculate various parameters characterizing the activity (e.g., duration of activity, total steps, distance traveled, intensity of activity, and calories burned). The monitor may identify a location on a subject where the monitor is worn, and provide a measure of the quality of the processed data or calculated parameters. The activity monitor may be configured to execute a self-calibration routine for an activity that is based on a temporary cessation of motion during the activity. Low energy paradigms are used to extend battery-powered operation of the activity monitor.

## Claims

### Claim 1

An activity monitor comprising:
an accelerometer; and
a microprocessor, wherein the microprocessor is configured to compute derivatives of first acceleration data received from a first axis of the accelerometer to form first acceleration-derivative values when the accelerometer is supported by a subject in motion and to process the acceleration-derivative values to identify an activity of the subject.

### Claim 2 (depends on 1)

The activity monitor of claim 1, further comprising:
a power source;
a transceiver; and
power-management circuitry configured to sense an activity level of the activity monitor and apply power to or remove power from at least the microprocessor responsive to the sensed activity level of the activity monitor.

### Claim 3 (depends on 1)

The activity monitor of claim 1, wherein the activity monitor includes a strap or a clip for attaching the activity monitor to an article of clothing or strapping the activity monitor to a subject.

### Claim 4 (depends on 1)

The activity monitor of claim 1, wherein the identified activity is one activity from among a plurality of activities identifiable by the activity monitor, the plurality of activities comprising two or more activities selected from the following group: walking, running, biking, swimming, using a type of exercise machine, rowing, cross-country skiing, jumping-jacks, sit-ups, push-ups, pull-ups, and jumping rope.

### Claim 5 (depends on 4)

The activity monitor of claim 4, wherein the microprocessor is further configured to identify, from the processed acceleration-derivative values, a falsified human activity and/or an activity that a human is not capable of performing.

### Claim 6 (depends on 1)

The activity monitor of claim 1, wherein the accelerometer is a multi-axis accelerometer and the microprocessor is further configured to compute derivatives of acceleration data received from each axis of the multi-axis accelerometer to form multi-axis acceleration-derivative values and to combine the multi-axis acceleration-derivative values according to the following relation:
D
n
=
∑
i
=
1
m



D
i
,
n

where Dn represents a combined acceleration-derivative value and Di,n represents an nth computed acceleration-derivative value for an ith axis of the multi-axis accelerometer.

### Claim 7 (depends on 1)

The activity monitor of claim 1, wherein the microprocessor is configured to:
form the first acceleration-derivative values into a first stream of data; and
identify from the first stream of data at least one characteristic feature by extracting peak values and/or peak widths from the first data stream.

### Claim 8 (depends on 1)

The activity monitor of claim 1, wherein the microprocessor is configured to:
form the first acceleration-derivative values into a first stream of data; and
identify from the first stream of data at least one characteristic feature by determining maximum and minimum values of the first data stream.

### Claim 9 (depends on 1)

The activity monitor of claim 1, wherein the microprocessor is further configured to:
form the first acceleration-derivative values into a first stream of data;
process the first stream of data to identify at least one characteristic feature of the first stream of data;
provide the at least one characteristic feature to a fuzzy inference engine; and
analyze the at least one characteristic feature with the fuzzy inference engine to identify one activity from among a plurality of different activities.

### Claim 10 (depends on 9)

The activity monitor of claim 9, wherein the microprocessor is further configured to:
provide the first acceleration data and/or the at least one characteristic feature to one of a plurality of activity analysis engines based on the identification of the one activity; and
calculate, with the one activity analysis engine, parameters characterizing the activity.

### Claim 11 (depends on 10)

The activity monitor of claim 10, wherein the parameters includes one or more parameters selected from the following group: a measure of pace of the activity, a measure of energy expended during the activity, a measure of distance traveled during the activity, and a duration of the activity.

### Claim 12 (depends on 11)

The activity monitor of claim 11, wherein the microprocessor is further configured to calculate at least one quality metric for one or more of the parameters, the at least one quality metric indicating a reliability of the one or more of the parameters.

### Claim 13 (depends on 1)

The activity monitor of claim 1, wherein the microprocessor is further configured to identify a location at which the activity monitor is supported by the subject and to select a data processing algorithm based on the identified location.

### Claim 14

A method comprising:
computing, by at least one microprocessor, derivatives of first acceleration data received from an accelerometer to form first acceleration-derivative values, the first acceleration data being representative of acceleration along a first axis of the accelerometer; and
processing the first acceleration-derivative values to identify an activity sensed by the accelerometer.

### Claim 15 (depends on 14)

The method of claim 14, further comprising:
computing derivatives of at least second acceleration data received from the accelerometer to form at least second acceleration-derivative values, the at least second acceleration data being representative of acceleration along at least a second axis of the accelerometer; and
combining the first and at least second acceleration-derivative values according to the following relation:
D
n
=
∑
i
=
1
m



D
i
,
n

where Dn represents a combined acceleration-derivative value and Di,n represents an nth computed acceleration-derivative value for an ith axis of the accelerometer.

### Claim 16 (depends on 14)

The method of claim 14, wherein the accelerometer is disposed in an activity monitor and the method further comprises clipping the activity monitor to an article of clothing or attaching the activity monitor to a subject.

### Claim 17 (depends on 14)

The method of claim 14, further comprising:
forming a first data stream of the first acceleration-derivative values over a first period of time;
identifying at least one characteristic feature of the first data stream;
providing the at least one characteristic feature to a fuzzy inference engine; and
analyzing the at least one characteristic feature with the fuzzy inference engine to identify one activity from among a plurality of different activities.

### Claim 18 (depends on 17)

The method of claim 17, wherein the identifying at least one characteristic feature comprises extracting peak values and/or peak widths from the first data stream.

### Claim 19 (depends on 17)

The method of claim 17, wherein the identifying at least one characteristic feature comprises determining maximum and minimum values of the first data stream.

### Claim 20 (depends on 17)

The method of claim 17, wherein the identifying at least one characteristic feature comprises calculating an average value of acceleration from the first acceleration data for the first period of time.

### Claim 21 (depends on 17)

The method of claim 17, wherein the plurality of different activities include two or more activities selected from the following group: walking, running, biking, swimming, using a type of exercise machine, rowing, cross-country skiing, jumping-jacks, sit-ups, push-ups, pull-ups, and jumping rope.

### Claim 22 (depends on 17)

The method of claim 17, wherein the plurality of different activities include falsified human activities.

### Claim 23 (depends on 17)

The method of claim 17, further comprising:
determining that the at least one characteristic feature is representative of an activity not recognized by the fuzzy inference engine; and
receiving machine-readable instructions and data enabling the fuzzy inference engine to subsequently identify an activity corresponding to the at least one characteristic feature.

### Claim 24 (depends on 17)

The method of claim 17, wherein the fuzzy inference engine uses historical data specific to a user in evaluating the at least one characteristic feature to identify the one activity from among the plurality of different activities.

### Claim 25 (depends on 17)

The method of claim 17, further comprising:
providing the first acceleration data and/or the at least one characteristic feature to one of a plurality of activity analysis engines based on the identification of the one activity; and
calculating, with the one activity analysis engine, parameters characterizing the activity.

### Claim 26 (depends on 25)

The method of claim 25, wherein the parameters includes one or more parameters selected from the following group: a measure of pace of the activity, a measure of energy expended during the activity, a measure of distance traveled during the activity, and a duration of the activity.

### Claim 27 (depends on 25)

The method of claim 25, further comprising calculating at least one quality metric for one or more of the parameters, the at least one quality metric indicating a reliability of the one or more of the parameters.