METHOD AND SYSTEM FOR MONITORING PHYSICAL ACTIVITIES OF A
USER
FIELD
10001] The present invention relates to a communication network. More particularly, the present invention relates to monitoring physical activities of a user using a portable electronic device in the communication network.
BACKGROUND
[0002] Bio-medical applications are very useful in measuring physiological changes in human beings. Conventional health monitoring devices can be used for Bio-medical applications that include measurement of the cardio rate, sugar level in human blood, blood pressure, calorie management, for example. Health of a user depends on two factors typically, one is a calorie intake of the user and another is a physical activity of the user. It is necessary to track the user continuously for precisely calculating health related results of the user. The health related results depend on the user's activities. Also, the health monitoring devices used for Bio-medical applications are equipped with testing probes, sensors, sharp clips and equipments that may be invasive and irritable to the human body.
[0003] However, the conventional health monitoring devices require manual intervention to monitor the physical activities of the user by providing inputs to the health monitoring devices very often. In addition, the current methods implemented to estimate energy expenditure of the user for a particular physical activity are cumbersome. Conventional technologies for remotely monitoring physical activities of the user are tedious and are prone to providing false results due to various factors, for example, improper functioning of the health monitoring device.
[0004] In light of the foregoing discussion, there is a need for a method and system to solve the above mentioned problems.

SUMMARY
[00051 Exemplary embodiments of the present invention relate to method and system for monitoring physical activities of a user.
|0006] In one embodiment, a method for monitoring physical activities of a user through portable electronic device includes obtaining acceleration data in three dimensions provided by an accelerometer sensor to obtain a predetermined parameter, the accelerometer sensor being present in the portable electronic device. The method also includes identifying physical-activity status of the user as motionless or in motion, if the predetermined parameter root mean square of 3-axes accelerometer data, is less than a first predetermined threshold value, wherein the physical-activity status is identified for monitoring physical activities of the user. The method includes determining median frequency from Power Spectrum Density curve of high pass filtered Z-axis acceleration data, where the Z-axis data from the accelerometer is the acceleration due to gravity. The method further includes identifying the physical-activity status of the user as slow motion, if the median frequency is less than a second predetermined threshold value, else identifying the physical-activity status of the user as fast motion.
[0007] In one embodiment, a system is a portable electronic device. The portable electronic device includes an accelerometer sensor, a storage unit for storing information and instructions and a processor responsive to the instructions. The processor is responsive to the instructions for obtaining 3D acceleration data provided by the accelerometer sensor to obtain a predetermined parameter, if the predetermined parameter is less than a first predetermined threshold value identifying physical-activity status of the user as motionless or in motion further idenfifying physical-activity status of a user as slow motion or fast motion, based on determining median frequency from Power Spectrum Density curve of high pass filtered Z-axis acceleration data, the Z-axis data being accelerafion due to gravity from accelerometer.
BRIEF DESCRIPTION OF THE DRAWINGS
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|0008] FIG. 1 is a block diagram of a portable electronic device, in accordance with an exemplary embodiment;
10009] FIG. 2 is a flowchart illustrating a method for monitoring physical activities of a user, in accordance with an embodiment of the present invention;
JOOIO] FIG. 3A and FIG. 3B is a flowchart illustrating a method for monitoring physical activities of a user, in accordance with another exemplary embodiment;
[0011] FIG. 4 shows exemplary graphical representations of Power Spectral Density curves for slow motion and fast motion, in accordance with an exemplary embodiment; and
(0012] FIG. 5 shows bar graphs of median frequencies of experimental samples for slow motion and fast motion, in accordance with an exemplary embodiment.
DETAILED DESCRIPTION
[0013] Exemplary embodiments of the present invention provide a method and
system for monitoring physical activities of a user.
[0014] FIG. 1 is a block diagram of a portable electronic device, in accordance with an exemplary embodiment. Examples of the portable electronic device include, but are not limited to, a mobile phone 105A, a smart phone, a pager and a Personal Digital Assistance (PDA). The mobile phone 105A includes a storage unit 110 for storing information and instructions. An example of the storage unit 110 includes but is not limited to a Random Access Memory (RAM). The mobile phone 105A also includes a processor 115. The processor 115 can include an integrated electronic circuit for processing and controlling functionalities of the mobile phone 105A. The mobile phone 105A also includes a three axes accelerometer sensor 135 (hereinafter the accelerometer sensor 135).
[0015] The processor 115 is responsive to the instructions for obtaining acceleration data in three dimensions provided by the accelerometer sensor 135 to obtain a predetermined parameter. The processor 115 is responsive to the instructions for
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identifying physical-activity status of the user as motionless, if the predetermined parameter is less than a first predetermined threshold value. The processor 115 is also responsive to the instructions for identifying physical-activity status of a user as slow motion or fast motion, based on determining median frequency from Power Spectrum Density curve of high pass filtered Z-axis (acceleration due to gravity) data.
[0016] In some embodiments, the functions performed by the processor 115 may also be performed by one or more units coupled to the processor 115. The one or more units may be internally or externally coupled to the processor 115.
(0017] The mobile phone 105A includes an input device 120. The input device 120 includes various keys, for communicating information to the processor 115. The information communicated to the processor 115 can be the information required for establishing a communication between the mobile phone 105A and a plurality of electronic devices, for example an electronic device 105B, and an electronic device 105C. The information can be communicated to the processor 115 from a machine-readable medium. The term machine-readable medium can be defined as a medium providing data to a machine to enable the machine to perform a specific function. The machine-readable medium can be a storage media. The storage media can be the storage unit 110. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into the machine.
[0018] The machine readable medium can also include online links, download links, and installation links providing the information to the processor 115.
[0019] The mobile phone 105A also includes a display unit 125. An example of the display unit 125 includes, but is not limited to a liquid crystal display (LCD).
[0020] In one embodiment, the display unit 125 is used to display a health care chart. The input device 120 can also be included in the display 125, for example a touch screen.
[0021] Various exemplary embodiments are related to the use of the mobile phone
105A for implementing the techniques described herein. In one embodiment, the
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techniques are performed by the processor 115 using information included in the storage unit 110.
[0022] In some exemplary embodiments of the present disclosure, the mobile phone
105A can be connected to the plurality of electronic devices through a network 130.
[0023] In one exemplary embodiment, the physical activities of the user are
monitored using the mobile phone 105A for determining energy expenditures of the user. The mobile phone 105A includes the accelerometer sensor 135 and the processor 115 for obtaining acceleration data in three dimensions to obtain a predetermined parameter. The acceleration data pertains to one or more signals monitored by the accelerometer sensor 135. The one or more signals are associated with body movements of the user. The one or more acceleration data are X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data. The X-axis acceleration data and Y-axis acceleration data provide linear acceleration associated with the user and the Z-axis acceleration data provides vertical acceleration associated with the user. A root mean square value of the X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data is the predetermined parameter.
[0024] The processor 115 identifies the user is at rest or is motionless if the predetermined parameter is below the first predetermined threshold value else person is in motion. The first predetermined threshold value is Ig. Further the processor 115 is also capable to identify the physical-activity status of the user as the slow motion or the fast motion based on determining the median frequency. The median frequency is determined from the Power Spectrum Density curve of high pass filtered Z-axis (acceleration due to gravity) data obtained from the accelerometer sensor 135. The processor 115 identifies the user is in slow motion or walking if the median frequency of the Power Spectrum Density curve is below the second predetermined threshold value. The second predetermined threshold value is 3.5 Hertz (Hz). If the median frequency of the Power Spectrum Density curve exceeds the second predetermined threshold value then the processor 115 identifies the user is in fast motion or running. Thus the processor 115 is capable of identifying the physical-activity status of the user by obtaining the acceleration
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data in one or more directions. An acceleration data is associated with the body movements of the user.
{00251 In an exemplary embodiment, the processor 115 is also capable of
determining an energy expenditure parameter of the user based on the physical-activity status of the user. The energy expenditure of the user is displayed in the display unit 125. The energy expenditure parameter is determined based on a plurality of parameters. The plurality of parameters are Physical Activity Level (PAL) parameter, Basal Metabolic Rate associated with the user and Thermal Energy Effect (TEF) associated with the user. The PAL parameter is based on the time period and the physical-activity status. The processor 115 generates a health care chart based on the energy expenditure parameter. The processor 115 monitors the physical activities of the user by referring to the health care chart. The health care chart can be broadcasted to various recipients, for example a user of the electronic device 105B, through the network 130.
[0026] The methodology for determining median frequency from the Power
Spectral Density curve and determining the energy expenditure of the user is well explained in conjunction with the FIG. 3.
[0027] FIG. 2 is a flowchart illustrating a method for monitoring physical activities of the user, in accordance with an embodiment of the present invention.
[0028J The method starts at step 205.
[0029] At step 210, the acceleration data is obtained in three dimensions provided by the accelerometer sensor 135 to obtain the predetermined parameter.
[0030] At step 215, a check is performed to determine if the predetermined parameter is less than the first predetermined threshold value. If the predetermined parameter is less than the first predetermined threshold value then step 220 is performed else step 225 is performed.
[0031] At step 220, the physical-activity status of the user is identified as motionless.
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[0032J At step 225, physical activity state is identified as mofion, for further classification as slow or fast motion, the median frequency from Power Spectrum Density curve of high pass filtered Z-axis (acceleration due to gravity) data is use.
10033] At step 230, a check is performed to determine if the median frequency is less than the second predetermined threshold value. If the median frequency is less than the second predetermined threshold value then step 235 is performed else step 240 is performed.
[0034] At step 235, the physical-activity status of the user is identified as slow motion.
[0035] At step 240, the physical-activity status of the user is identified as fast motion.
[0036] The method stops at step 245.
]0037] FIG. 3A and FIG. 3B is a flowchart illustrating a method for monitoring physical activities of the user, in accordance with another exemplary embodiment.
[0038] The method starts at step 305.
]0039] At step 310, the acceleration data are obtained in three dimensions provided by the accelerometer sensor 135 to obtain the predetermined parameter. An acceleration data is associated with body movements of the user. The one or more acceleration data are X-axis acceleration data, Y-axis acceleration data and Z-axis acceleration data. The X-axis acceleration data and Y-axis acceleration data provide linear acceleration associated with the user and the Z-axis acceleration data provides vertical acceleration associated with the user.
[0040] At step 315, a check is performed to determine if the predetermined parameter is less than the first predetermined threshold value. If the predetermined parameter is less than the first predetermined threshold value then step 320 is performed else step 325 is performed. The first predetermined threshold value is Ig. The root mean square value of the X-axis acceleration data, the Y-axis acceleration data and the Z-axis acceleration data is the predetermined parameter.
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10041] At step 320, the physical-activity status of the user is identified as motionless.
10042] At step 325, physical activity state is identified as motion, for further classification as slow or fast motion, the median frequency from Power Spectrum Density curve of high pass filtered Z-axis acceleration data is determined. The Power Spectrum Density curve provides an average power of the high pass filtered Z-axis acceleration data versus frequency. The Power Spectrum Density curve is determined using Welch method.
[0043] In an exemplary embodiment, acceleration data of Z- axis or the Z-axis acceleration data have two components during motion, one is body movement component and another is gravity component which is present due to gravity. The body movement component and the gravity movement component overlap in both time and frequency domain. The body movement component and the gravity movement component are separated to use the body movement of the user for identifying the physical-activity status associated with the user. Normally gravity component signal has lower frequency range, and high pass filter parameters are used to remove the gravity component from the acceleration data of Z- axis. For example, the high pass filter of IIR Elliptical filter, with cut-off frequency (fc) 0.5 Hz, order of the filter (n) 7. Pass band ripple (Rp) O.OldB and Stop band Ripple (Rs) lOOdB. The Power Spectrum Density obtained from the Z-axis acceleration data is used to find out the frequency component corresponds to motion due to at least one physical-activity of the user. For example, the at least one physical activity of the user may be the slow motion or the fast motion.
10044] In one embodiment, the Welch method is used to determine the Power Spectrum Density. Area under the Power Spectrum Density curve represents the total average power (Pav) which is given by equation (1):
Length of P^^
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Where fs is the sampling frequency of the signal, Pxx is the Power Spectrum Density value in dB/Hz for a particular frequency and Length of Pxx is the number of points for which Pxx is calculated.
[0045| The median frequency is determined using the total average power (Pav) derived from the Power Spectrum Density. The median frequency is a frequency value which divides the Power Spectrum Density curve into equal parts.
[0046] In one aspect, experimental results show that the median frequency for the fast motion the range of 3.5 - 4.5Hz, and for the slow motion the range of 2.5-3.5 Hz.
[0047] At step 330, a check is performed to determine if the median frequency is less than the second predetermined threshold value. If the median frequency is less than the second predetermined threshold value then step 335 is performed else step 340 is performed. The second predetermined threshold value is 3.5 Hz.
[0048] At step 335, the physical-activity status of the user is identified as the slow motion.
[0049] At step 340, the physical-activity status of the user is identified as the fast motion
[0050] At step 345, the time period associated with the physical-activity status is continuously measured. For example, if the physical-activity status of the user is the slow motion then the time period for which the user was in the slow motion is measured or monitored.
[0051] At step 350, the health care chart for the user is generated by determining the energy expenditure parameter. The energy expenditure parameter is determined based on the plurality of parameters. The plurality of parameters include Physical Activity Level (PAL) parameter, the PAL parameter is based on the time period and the physical-activity status. Basal Metabolic Rate associated with the user and Thermal Energy Effect (TEF) associated with the user. The Basal Metabolic Rate is the energy needed by the body for
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maintenance of life at rest. The PAL parameter is the average energy estimated during the daily physical activities of the user. The TEF is the 10% of calories of food consumed.
|0052] In some embodiments, the energy expenditures are determined or measured for 24 hours. The Basal Energy Rate is calculated using body weight as given in the reference "Staci Nix, "Williams' basic nutrition and diet therapy", 2''' Edition, Elsevier Mosby". The PAL parameter is calculated in factors of Basal Metabolic Rate (BMR) depending on the duration and the physical-activity status.
[0053] The energy expenditure parameter is calculated using the following equation, in accordance with an example:
The energy expenditure parameter = (BMR x PAL + TEF).
[0054] At step 355, the physical activities of the user are monitored by referring to the health care chart. The health care chart may display details pertained the user.
[00551 For example, the health care chart may display the following details pertained to the user: A man weighs 59 Kg (with BMR = 1274) who eats 3000 Kcal food per day and maintain 1 hour daily walking as detected by activity classification approach, the PAL parameter for this duration of activity is 1.53 as given in the reference "Staci Nix, "Williams' basic nutrition and diet therapy", ^'^ Edition, Elsevier Mosby", so total energy expenditure will be Total Energy expenditure = 1274 x 1.53 + 220 (BMR x PAL + TEF )= 2169.22 Kcal per day.
[0056] Conclusion in the health care chart may be: The user needs to extend the physical-activity status or reduce the Kcal intake for healthy living.
[0057] At step 360, the health care chart is displayed to the user in the mobile phone 105A. On generating the health care chart by the processor 115 the user can view the health care chart on the display unit 125.
[0058[ At step 365, the health care chart is sent to one or more recipients. For example, the health care chart is displayed to the user in the mobile phone 105A may be sent to an electronic device 105B through the network 130 using one or more protocols.
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Examples of the protocols include, but are not limited to, an internet protocol, short service messaging of mobile communication .
[00591 The method stops at step 370.
|0060| FIG. 4 shows exemplary graphical representations of power spectral density curves for slow motion and fast motion, in accordance with an exemplary embodiment.
[0061] As shown in a graph 405, the total average power (Pav) for the slow motion or walking is 0.0843 dB, which is calculated using the equation (1) and median frequency is 2.7 Hz.
[0062] As shown in a graph 410, the total average power (Pav) for the fast motion or running is 1.519dB and median frequency is 4.3 Hz.
[0063] FIG. 5 shows bar graphs of median frequencies for the slow motion and the fast motion at various samples, in accordance with an exemplary embodiment.
[0064] The bar graphs of median frequencies for the slow motion and the fast
motion are the experimented values obtained by wearing the accelerometer sensor 135 during the slow motion or the fast motion.
[0065] As shown in FIG. 5 the average value of median frequency and its Standard
deviation for the slow motion and the fast motion are calculated for 10 experiment samples.
[0066] The average median frequency, as shown in a bar graph 505, for the slow
motion is 3.107 Hz with standard deviation (S.D.) of around 0.534, where as for the fast motion, as shown in a bar graph 510, the average median frequency is 4.013 Hz with S.D. ofO.627.
10067] The Median frequency of threshold 3.5Hz is used to identify motion as the slow motion and the fast motion from the experiment results.
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(0068J Various embodiments of the invention provide a method and system for
monitoring physical activities of a user. The present invention is capable of identifying the physical-activity status, for example the motionless, the slow motion or the fast motion using the one or more acceleration data. The present invention is also capable of determining the energy expenditure parameter based on the physical-activity status identified. The above mentioned details guide the user to monitor his energy expenditure parameter as per World Health Organization (WHO) recommendation and plan his physical activities for better health. The present invention also helps to monitor the physical activities of the person remotely, who is undergoing rehabilitation program for recovery of an injury.
[0069] In the preceding specification, the present invention and its advantages have
been described with reference to specific embodiments. However, it will be apparent to a person of ordinary skill in the art that various modifications and changes can be made, without departing from the scope of the present invention, as set forth in the claims below. Accordingly, the specification and figures are to be regarded as illustrative examples of the present invention, rather than in restrictive sense. All such possible modifications are intended to be included within the scope of the present invention.
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CLAIMS
I/We claim:
1. A method for monitoring physical activities of a user of a portable electronic device,
the method comprising:
obtaining acceleration data in three dimensions provided by an accelerometer sensor to obtain a predetermined parameter, the accelerometer sensor being present in the portable electronic device;
identifying physical-activity status of the user as motionless, if the predetermined parameter is less than a first predetermined threshold value, the physical-activity status is identified for monitoring physical activities of the user;
determining median frequency from Power Spectrum Density curve of high pass filtered Z-axis acceleration data, wherein the Z-axis acceleration data being acceleration due to gravity; and
identifying the physical-activity status of the user as slow motion, if the median frequency is less than a second predetermined threshold value, else identifying the physical-activity status of the user as fast motion.
2. The method as claimed in claim 1, wherein the one or more acceleration data pertains
to one or more signals monitored by the accelerometer sensor, the one or more signals are associated with body movements of the user.
3. The method as claimed in claim 1, wherein the predetermined parameter is root mean
square value of the one or more acceleration data.
4. The method as claimed in claim 1, wherein the Z-axis acceleration data provides
acceleration associated with the user in vertical direction.

5. The method as claimed in claim 1, wherein the Power Spectrum Density curve
provides average power of the high pass filtered Z-axis acceleration data versus frequency.
6. The method as claimed in claim 5, wherein, the Power Spectrum Density curve is
determined using Welch method.
7. The method as claimed in claim 1, wherein the median frequency is a frequency value
which divides the Power Spectrum Density curve into equal parts.
8. The method as claimed in claim 1 further comprising:
continuously measuring time period associated with the physical-activity status.
9. The method as claimed in claim 1, wherein the first predetermined threshold value is
Ig-
10. The method as claimed in claim 1, wherein the second predetermined threshold value
is: 3.5 Hertz.
11. The method as claimed in claim 1 further comprising:
generating a health care chart for the user by determining an energy expenditure parameter, the energy expenditure parameter is determined based on a plurality of parameters; and monitoring the physical activities of the user by referring to the health care chart.
12. The method as claimed in claim 11, wherein the plurality of parameters are at least
one of
Physical Activity Level (PAL) parameter, the PAL parameter is based on the time
period and the physical-activity status;
Basal Metabolic Rate associated with the user; and
Thermal Energy Effect (TEF) associated with the user.

13. The method as claimed in claim 11 further comprising:
displaying the heahh care chart to the user; and sending the health care chart to one or more recipients.
14. A portable electronic device comprising:
an accelerometer sensor;
a storage unit for storing information and instructions; and
a processor responsive to the instructions for:
acceleration data in three dimensions provided by the accelerometer sensor to obtain a predetennined parameter;
identifying physical-activity status of the user as motionless, if the predetermined parameter is less than a first predetermined threshold value; and
identifying physical-activity status of a user as slow motion or fast motion, based on determining median frequency from Power Spectrum Density curve of high pass filtered Z-axis acceleration data, the Z-axis acceleration data being acceleration data due to gravity.
15. A method for monitoring physical activities of a user using a portable electronic
device, the portable electronic device as described herein and in accompanying figures.
16. The portable electronic device for performing a method, the method as described
herein and in accompanying figures.