`
`USOO7653508B1
`
`(12) United States Patent
`Kahn et a].
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 7,653,508 B1
`Jan. 26, 2010
`
`(54) HUMAN ACTIVITY MONITORING DEVICE
`
`(75)
`
`Inventors: Philippe Kahn. Aptos. CA (US):
`Arthur Kinsolving. Santa Cruz. CA
`(US); Mark Andrew Christensen. Santa
`Cruz. CA (US): Brian Y. Lee. Aptos. CA
`(US): David Vogel. Santa Cruz. CA (US)
`
`(73) Assignee: DP Technologies, Inc.. Scotts Valley. CA
`(US)
`
`( * ) Notice:
`
`Subject to any disclaimer. the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 556 days.
`
`(21) Appl.No.: 11/644,455
`
`(22)
`
`Filed:
`
`Dec. 22, 2006
`
`(51)
`
`Int. Cl.
`(2006.01)
`G018 21/02
`(2006.01)
`0013 7/00
`(2006.01)
`G01C 22/00
`(2006.01)
`G06F 19/00
`(2006.01)
`(1'06F I 7/40
`(52) U.S. Cl.
`............................. 702/160: 33/700: 377/1:
`377/13: 377/25: 377/242; 702/1: 702/127:
`702/155: 702/158; 702/187: 702/189
`(58) Field ofClassitication Search ................... 33/700.
`33/701: 73/1.01. 1.37. 1.38. 1.75. 1.76. 1.77.
`73/1.78.1.79.1.81: 3770.13.15.17. 20.
`377/24. 24.1. 24.2: 702/1. 85. 97. 127. 141.
`702/150. 155. 158. 160. 187. 189
`See application file for complete search history.
`
`(56)
`
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`
`(57)
`
`ABSTRACT
`
`A method for monitoring human activity using an inertial
`sensor includes continuously determining an orientation of
`the inertial sensor. assigning a dominant axis. updating the
`dominant axis as the orientation of the inertial sensor
`
`changes. and counting periodic human motions by monitor-
`ing accelerations relative to the dominant axis.
`
`20 Claims. 9 Drawing Sheets
`
`
`
`
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`................. 702/ 141
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`.............. 340/573.1
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`......................... 377/24.2
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`
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`
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`tional Application No. PCT/US2008/072537, mailed Oct. 22, 2008,
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`
`Mizell, David, “Using Gravity to Estimate Accelerometer Orienta-
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`
`erg, 2003, 2 pages.
`
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`
`Weinberg, Harvey, “MEMS Motion Sensors Boost Handset Reliabil-
`
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`
`ity”, Jun. 2006,
`.mwrf.com/Articles/Print.cfm?ArticleID:12740,
`
`
`
`
`
`Feb. 21, 2007, 4 pages.
`
`
`
`
`
`
`* Cited by examiner
`
`
`
`
`
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`

`U.S. Patent
`
`Jan. 26, 2010
`
`Sheet 1 on
`
`Us 7,653,508 B1
`
`Dominant Axis Logic m
`
`Dominant
`Axis Setting
`Logic
`
`Rolling Average
`Logiclafi "
`:
`
`Cadence Logic
`
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`
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`
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`
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`
`Measurement
`Selection
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`
`Threshoid
`
`Comparator
`
`Comparator
`
`Step Counting Logic 1_3_Q
`
`Electronic Device mg
`
`Figure 1
`
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`

`

`U.S. Patent
`
`Jan. 26, 2010
`
`Sheet 2 of 9
`
`US 7,653,508 B1
`
`_
`
` A
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`

`

`U.S. Patent
`
`Jan. 26, 2010
`
`Sheet 3 of9
`
`US 7,653,508 B1
`
`300A
`
`0» Initial State
`
`
`Sleep Mode 3 t’\
`
`
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`
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`
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`
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`Figure 3
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`US. Patent
`
`Jan. 26, 2010
`
`Sheet 4 of9
`
`us 7,653,508 Bl
`
`400A
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`Figure 4
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`U.S. Patent
`
`Jan. 26, 2010
`
`Sheet 5 of 9
`
`US 7,653,508 B1
`
`
`
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`Figure 5
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`US. Patent
`
`Jan. 26, 2010
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`Sheet 6 of9
`
`US 7,653,508 B1
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`615
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`Figure 6
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`U.S. Patent
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`Jan. 26, 2010
`
`Sheet 7 m9
`
`US 7,653,508 B1
`
`700A
`
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`Step Timer
`29.5.
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`Figure 7
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`U.S. Patent
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`Jan. 26, 2010
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`Sheet 8 of9
`
`US 7,653,508 B1
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`‘/////_\\ 800
`
`Take Measurement(s) of Acceleration Data so;
`
`Filter Measurement(s) m
`
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`
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`
`Figure 8
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`U.S. Patent
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`Jan. 26, 2010
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`Sheet 9 of9
`
`US 7,653,508 B1
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`900 d
`
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`
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`
`Figure 9
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`

`
`
`US 7,653,508 B1
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`
`
`1
`
`HUMAN ACTIVITY MONITORING DEVICE
`
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`FIELD OF THE INVENTION
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`2
`
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`FIG. 9 illustrates a flow diagram for a method of orienting
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`an inertial sensor, in accordance with one embodiment of the
`
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`present invention.
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`This invention relates to a method of monitoring human
`
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`activity, and more particularly to counting periodic human
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`motions such as steps.
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`BACKGROUND
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`The development of Micro-Electro-Mechanical Systems
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`(MEMS) technology has enabled manufacturers to produce
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`inertial sensors (e.g., accelerometers) of sufficient size, cost,
`and power consumption to fit into portable electronic devices.
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`Such inertial sensors can be found in a limited number of
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`commercial electronic devices such as cellular phones, por-
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`table music players, pedometers, game controllers, and por-
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`table computers.
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`Step counting devices are used to monitor an individual’s
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`daily activity by keeping track of the number of steps that he
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`or she takes. Generally, step counting devices that utilize an
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`inertial sensor to measure motion to detect steps require the
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`user to first position the device in a limited set of orientations.
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`In some devices, the required orientations are dictated to the
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`user by the device. In other devices, the beginning orientation
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`is not critical, so long as this orientation can be maintained.
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`Step counting devices are often confused by motion noise
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`experienced by the device throughout a user’s daily routine.
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`This noise causes false steps to be measured and actual steps
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`to be missed in conventional step counting devices. Conven-
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`tional step counting devices also fail to accurately measure
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`steps for individuals who walk at a slow pace. Such step
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`counting devices can fail to operate for seniors and others
`walking at a slow pace.
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`BRIEF DESCRIPTION OF THE DRAWINGS
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`The present invention is illustrated by way of example, and
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`not by way of limitation, and can be more fully understood
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`with reference to the following detailed description when
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`considered in connection with the following figures:
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`FIG. 1 is a block diagram illustrating one embodiment of
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`an electronic device;
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`FIG. 2 illustrates an exemplary cadence of motion graph
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`that measures time versus acceleration, in accordance with
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`FIG. 3 shows a state diagram for the behavior of a system
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`of monitoring human activity using an inertial sensor, in
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`accordance with one embodiment of the present invention;
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`FIG. 4 illustrates a flow diagram for a method of operating
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`an electronic device in sleep mode, in accordance with one
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`FIG. 5 illustrates a flow diagram for a method of operating
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`an electronic device in entry mode, in accordance with one
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`FIG. 6 illustrates a flow diagram for a method of operating
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`an electronic device in stepping mode, in accordance with one
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`FIG. 7 illustrates a flow diagram for a method of operating
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`an electronic device in exit mode, in accordance with one
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`embodiment of the present invention;
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`FIG. 8 illustrates a flow diagram for a method of recogniz-
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`ing a step in accordance with one embodiment of the present
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`invention, in accordance with one embodiment of the present
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`invention; and
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`DETAILED DESCRIPTION
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`Embodiments of the present invention are designed to
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`monitor human activity using an inertial sensor. In one
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`embodiment, a dominant axis is assigned after determining an
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`orientation of an inertial sensor. The orientation ofthe inertial
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`sensor is continuously determined, and the dominant axis is
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`updated as the orientation of the inertial sensor changes. In
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`one embodiment, periodic human motions are counted by
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`monitoring accelerations relative to the dominant axis.
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`FIG. 1 is a block diagram illustrating an electronic device
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`in accordance with one embodiment of the present
`100,
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`invention. The electronic device 100 in one embodiment
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`comprises an acceleration measuring logic 105, a filter 120, a
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`dominant axis logic 127, a step counting logic 130, a timer
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`170, and a final step count 175. In one embodiment, the
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`electronic device 100 is a portable electronic device that
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`includes one or more inertial sensors. The inertial sensors
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`may measure accelerations along a single axis or multiple
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`axes. The inertial sensors may measure linear as well as
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`rotational (angular) accelerations. The electronic device 100
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`may be used to count steps or other periodic human motions.
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`Steps may be accurately counted regardless of the placement
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`and/or orientation of the device on a user. Steps may be
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`accurately counted whether the electronic device 100 main-
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`tains a fixed orientation or changes orientation during opera-
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`tion. The electronic device 100 may be carried in a backpack,
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`pocket, purse, hand, or elsewhere, and accurate steps may still
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`be counted.
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`The acceleration measuring logic 105 measures accelera-
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`tion data at a sampling rate. The sampling rate may be fixed or
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`variable. In one embodiment, the acceleration measuring
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`logic 105 receives a timing signal from the timer 170 in order
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`to take measurements at the sampling rate. The acceleration
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`measuring logic 105 may be an inertial sensor.
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`In one embodiment, measurement data is processed by the
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`filter 120 to remove noise. The filter 120 may be implemented
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`in hardware, software, or both hardware and software. The
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`filter 120 may include a high pass filter, a low pass filter, a
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`bandpass filter, a bandstop filter and/or additional filters. The
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`filter 120 may include a digital filter and/or an analog filter. In
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`one embodiment, a hardware digital filter includes at least one
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`ofa finite impulse response (FIR) filter and an infinite impulse
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`response (IIR) filter. In one embodiment, an N-tap hardware
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`digital FIR filter is used. The use of a hardware FIR filter may
`reduce power consumption by reducing and/or eliminating
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`software digital filtering.
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`In one embodiment, the filter 120 includes multiple filters,
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`and a determination of which filters to apply to the measure-
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`ment data is made based upon an operating mode of the
`electronic device 100. In one embodiment, the selection of
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`which filters to use is determined by the type of user activity
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`detected. For example, a low pass filter may be used to remove
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`high frequency noise that would interfere with step counting
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`when a user is walking. In contrast, a high pass filter may be
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`used when quick motions are to be monitored.
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`Filtered measurement data may be passed on to the domi-
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`nant axis logic 127 and the step counting logic 130. In one
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`embodiment, the dominant axis logic 127 includes a cadence
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`logic 132, a rolling average logic 135, and a dominant axis
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`setting logic 140. In an alternative embodiment, more or
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`3
`fewer logics may be used to determine a dominant axis.
`Alternative means of identifying a dominant axis may be used
`in other embodiments.
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`window may be set based on the period and/or cadence ofthe
`actual motion cycle (e.g., a stepping period), on set limits,
`and/or on other determiners.
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`4
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`US 7,653,508 B1
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`In one embodiment, the dominant axis logic 127 is used to
`determine an orientation of the electronic device 100 and/or
`an inertial sensor within the electronic device 100. In alter-
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`5
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`native embodiments, other logics may be used to determine
`an orientation of the electronic device 100.
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`Referring to FIG. 2, an exemplary first cadence window
`240 and second cadence window 255 are shown. The first
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`cadence window 240 may be defined by a first cadence win-
`dow minimum 230 and a first cadence window maximum
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`10
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`235. The second cadence window 255 may be defined by a
`second cadence window minimum 245 and a second cadence
`window maximum 250. In one embodiment, the cadence
`window minimums 230 and 245 and cadence window maxi-
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`Referring to FIG. 1, the cadence logic 132 may determine
`one or more sample periods to be used by the rolling average
`logic 135, and may determine a cadence window 150 to be
`used by the step counting logic 130. In one embodiment, the
`cadence logic 135 detects a period and/or cadence of a motion
`cycle. The period and/or cadence of the motion cycle may be
`based upon user activity (e.g. rollerblading, biking, running,
`walking, etc).
`Many types ofmotions that are useful to keep track ofhave
`a periodic set of movements. Specific periodic human
`motions may be characteristic of different types of user activ-
`ity. For example, to walk, an individual must lift a first leg,
`move it forward, plant it, then repeat the same series of
`motions with a second leg. In contrast, a person rollerblading
`performs a repeated sequence ofpushing, coasting and liftoff
`for each leg. For a particular individual, the series of walking
`motions will usually occur in about the same amount oftime,
`and the series of rollerblading motions will usually occur in
`the same amount of time. The repeated set of motions can be
`considered a unit, and defines the motion cycle. The amount
`of time that it takes to complete one motion cycle defines the
`motion cycle’s period, and the number of motion cycles that
`occur in a given unit of time define the motion cycle’s
`cadence. For simplicity, the term “step” is used in this appli-
`cation to describe the user activity being evaluated. However,
`in the context of this application, the term “step” should be
`taken to mean any user activity having a periodic set of
`repeated movements.
`FIG. 2 illustrates an exemplary motion cycle graph 200 that
`measures time versus acceleration, in accordance with one
`embodiment ofthe present invention. The exemplary motion-
`cycle graph 200 shows acceleration data taken with a single
`tri-axis inertial sensor. The acceleration at a given period of
`time is represented for a first axis 203 of an inertial sensor(s),
`a second axis 205 ofthe inertial sensor(s), and a third axis 207
`of the inertial sensor(s). In one embodiment, the cadence
`logic 132 of FIG. 1 analyzes the acceleration along the first
`axis 203, second axis 205 and third axis 207 to detect a motion
`cycle. Once a motion cycle is detected, a period of the motion
`cycle is determined, and a cadence of the motion cycle is
`determined. FIG. 2 shows an exemplary period of a motion
`cycle 215 for the third axis 207, the period being approxi-
`mately 0.6 seconds. The same period can also be seen to a
`lesser degree in the second axis 205 and the first axis 203. The
`corresponding cadence to the motion cycle is approximately
`one hundred motion cycles per minute.
`In one embodiment, once a stepping period (or other
`motion cycle period) is determined, that period may be used
`to set the cadence window (the allowable time window for
`steps to occur). In one embodiment, the period is updated
`after each step. The current stepping period may be a rolling
`average of the stepping periods over previous steps, as dis-
`cussed in more detail with reference to the rolling average
`logic 135 of FIG. 1.
`A cadence window may be used to facilitate accurate mea-
`surement of a step, or other periodic human motion. A
`cadence window is a window of time since a last step was
`counted that is looked at to detect a new step. A cadence
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`mums 235 and 250 are determined by measuring lengths of
`time since the most recent step was counted. In one embodi-
`ment, this length oftime is measured via the timer 170 of FIG.
`1. In other embodiments, other variables may be used to set
`the cadence window. For example, cadence windows may be
`determined by measuring cumulative amounts of accelera-
`tion that have been measured since the previous step was
`counted.
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`Returning to FIG. 2, cadence windows may be used to
`count steps until an expected step is not encountered. In one
`embodiment, new cadence windows are determined periodi-
`cally. In one embodiment, the cadence window is a dynamic
`cadence window that continuously updates as a user’s
`cadence changes. For example, using a dynamic cadence
`window, a new cadence window length may be set after each
`step. The cadence window minimums may be determined by
`subtracting a value from the stepping period, and the cadence
`window maximums may be determined by adding a value to
`the stepping period. In one embodiment, the cadence window
`maximums are preset, and the cadence window minimums
`are updated after each step is counted. In one embodiment, the
`cadence window minimums are preset, and the cadence win-
`dow maximums are updated after each step is counted. In one
`embodiment, both the cadence window minimums and
`cadence window maximums are updated when a step is
`counted. In one embodiment, the current cadence window
`minimum is determined by subtracting 200 ms from the cur-
`rent stepping cadence period.
`In one embodiment,
`the
`cadence window minimum has a minimum value of 240 ms.
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`In the illustrated embodiment of FIG. 2, a first step 217 is
`counted at 0.65 seconds, and a second step 232 is counted at
`approximately 1.15 seconds. The first cadence window 240
`opens at approximately 0.4 seconds from the first step 217,
`and closes at approximately 0.8 seconds from the first step
`217. As shown, the second step 232 falls within the first
`dynamic cadence window 240. A third step 233 falls within
`the second dynamic cadence window 255, which may have a
`second cadence window minimum 245 and second cadence
`window maximum 250 that are different from the first
`cadence window minimum 230 and first cadence window
`maximum 235. The illustrated second cadence window mini-
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`mum is about 0.35 seconds from the second step 232, and the
`second cadence window maximum 250 is about 0.75 seconds
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`from the second step 232. Other cadence window minimums
`and maximums are also possible. When motion criteria (e. g.,
`threshold conditions) are met within a cadence window, a step
`is detected, whereas when motion criteria are met outside of
`the cadence windows no step is detected.
`Ifno previous steps have been detected, there is no cadence
`minimum, and a step may be detected at any time that motion
`criteria are met. If fewer than the required number of steps to
`determine a dynamic cadence window have been detected,
`then the cadence window may have a default minimum and
`maximum value. In one embodiment, the cadence window
`has a default minimum of around 325 ms and a default maxi-
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`mum of around 1000 ms. Once enough steps have been
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`5
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`detected to determine a dynamic stepping cadence or period,
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`the cadence window may be set to the determined stepping
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`period plus or minus an error factor. In one embodiment, a
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`count of between about two to about ten periodic human
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`motions is suflicient to set a dynamic cadence window.
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`The cadence of any periodic human motion will generally
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`not change more than a certain amount in a given time period.
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`In one embodiment, the cadence window may be sufficiently
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`wide to continue counting periodic human motions even
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`when a stepping cadence changes. In one embodiment, the
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`cadence window is narrower, and steps may not be counted
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`when a stepping cadence changes. So as not to miss steps,
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`once a new stepping cadence is detected, previous measure-
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`ments may be examined to determine whether they register as
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`steps under the new stepping cadence and a new cadence
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`window. Therefore, steps may be counted even if they did not
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`occur in the original cadence window. The cadence window
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`may update dynamically to a user’s actual cadence. Human
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`cadences change within a known window of rates, and so
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`steps can be differentiated from other noise. This may ame-
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`liorate and/or eliminate missed step counts due to changes in
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`cadence.
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`6
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`Alternatively, the rolling averages may be maintained by
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`buffering the measurements used to calculate the rolling aver-
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`ages. As the buffers fill, oldest measurement data can be
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`discarded and replaced by new measurement data. The mea-
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`surements in the buffer can be averaged after each measure-
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`ment to determine a new rolling average.
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`In one embodiment, the dominant axis setting logic 140
`determines an orientation of the electronic device 100 and/or
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`the inertial sensor(s) within the electronic device 100. The
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`orientation may be determined based upon the rolling aver-
`ages of accelerations created by the rolling average logic 135.
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`In one embodiment, once the orientation is determined, a
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`dominant axis is assigned based upon the orientation. Deter-
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`mining an orientation of the electronic device 100 may
`include identifying a gravitational influence. The axis with
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`the largest absolute rolling average may be the axis most
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`influenced by gravity, which may change over time (e. g. as
`the electronic device is rotated). Therefore, a new dominant
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`axis may be assigned when the orientation of the electronic
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`device 100 and/or the inertial sensor(s) attached to or embed-
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`ded in the electronic device 100 changes.
`In one embodiment, the actual axis with the largest abso-
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`lute rolling average over the sample period is assigned as the
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`dominant axis. In alternative embodiments, the dominant axis
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`does not correspond to one of the actual axes of the inertial
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`sensor(s) in a current orientation, but rather to an axis that is
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`defined as approximately aligned to gravity. In one embodi-
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`ment, the dominant axis corresponds to a virtual axis that is a
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`component of a virtual coordinate system. In one embodi-
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`ment, the dominant axis setting logic 140 assigns the domi-
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`nant axis by performing a true gravity assessment, such as by
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`doing trigonometric calculations on the actual axes based on
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`the gravitational influence. In one embodiment, the dominant
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`axis setting logic 140 assigns the dominant axis by comparing
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`the gravitational influence to a data structure such as a lookup
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`table, associative array, hash table, adjacency matrix, etc.
`Returning to FIG. 1, the step counting logic 130 may
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`include a measurement selection logic 145, a cadence win-
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`dow 150, a measurement comparator 155, a threshold com-
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`parator 160, a step count buffer 165, and a mode logic 190.
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`The measurement selection logic 145 may determine which
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`measurements from the measurement buffer to use to deter-
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`mine if a step has occurred. In one embodiment, the measure-
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`ment selection logic 145 may monitor accelerations relative
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`to the dominant axis, and select only those measurements
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`with specific relations to the dominant axis for measurement.
`For example, only accelerations that are approximately par-
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`allel to the dominant axis may be selected, or alternatively,
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`only accelerations that are approximately perpendicular to
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`the dominant axis may be selected. In one embodiment, the
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`measurement selection logic 145 selects only measurements
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`of acceleration data along the dominant axis. In alternative
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`embodiments, measurements of acceleration data along other
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`axes may also be used. In one embodiment, measurements of
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`acceleration along only the other axes are used.
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`Selected measurements may be forwarded to the measure-
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`ment comparator 155 and the threshold comparator 160 to
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`determine whether a step has occurred. The measurement
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`comparator 155 may compare a current measurement to pre-
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`vious measurements. Based on this comparison, a current
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`measurement may qualify as a step if it has met certain com-
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`parison cr