Method and apparatus for determination of kinematic parameters of mobile device user
Some embodiments of the invention provide methods and apparatus for generating a user's contextual information using a mobile or wearable device. In some embodiments, obtaining the user's contextual information comprises obtaining sensors information, and applying a transformation to the sensors signals, wherein the transformation to the sensors signals comprises the use of wavelets, and the sensors comprise an accelerometer.
1. A method for monitoring a mobile device user in real time, the method comprising the following steps:
reading data from an accelerometer within the mobile device;
obtaining energies of wavelet transformation coefficients of the accelerometer data;
weighting the energies of wavelet transformation coefficients; obtaining a summation of said weighted energies, and leverage said summation to estimate a velocity of the user of the device;
leveraging the weighted energies of the wavelet transformation coefficients to choose the coefficients from which to obtain a reconstructed wave from where each stride of the user is clearly identified;
combining stride time information with the velocity estimation to estimate stride length.
2. The method of claim 1 , further comprising:
presenting an instantaneous value of said velocity on the mobile device in real time.
3. The method of claim 2 , wherein all the steps are performed in the mobile device without relying on elements external to the mobile device.
4. The method of claim 3 , further comprising:
dynamic selection of accelerometer sampling frequency and wavelet transformation parameters, comprising levels of decomposition, mother wavelet, and processing time window.
5. The method of claim 1 , further comprising:
combining the user's velocity with other information for enhanced localization and calibration.
6. The method of claim 1 , further comprising:
presenting on the mobile device a moving object whose movement is controlled by the obtained velocity.
7. An apparatus comprising:
a processor;
a non-transitory processor-readable medium including one or more instructions which, when executed by the processor, causes the processor to monitor a mobile device user in real time by the steps of:
reading data from an accelerometer within the mobile device;
obtaining energies of wavelet transformation coefficients of the accelerometer data;
weighting the energies of wavelet transformation coefficients; obtaining a summation of said weighted energies, and leverage said summation to estimate a velocity of the user of the device;
leveraging the weighted energies of the wavelet transformation coefficients to choose the coefficients from which to obtain a reconstructed wave from where each stride of the user is clearly identified;
combining stride time information with the velocity estimation to estimate stride length.
8. The apparatus of claim 7 , further comprising:
presentation of an instantaneous value of said velocity on the mobile device in real time.
9. The apparatus of claim 8 , further comprising:
dynamic selection of accelerometer sampling frequency and wavelet transformation parameters, comprising levels of decomposition, mother wavelet, and processing time window.
10. The apparatus of claim 7 , further comprising:
combination of the user's velocity with other information for enhanced localization and calibration.
11. The apparatus of claim 7 , further comprising:
presentation on the mobile device of a moving object whose movement is controlled by the obtained velocity.
12. A non-transitory processor-readable medium including instructions which, when executed by a processor, causes the processor to monitor a mobile device user in real time by the steps of:
reading data from an accelerometer within the mobile device;
obtaining energies of wavelet transformation coefficients of the accelerometer data;
weighting the energies of wavelet transformation coefficients; obtaining a summation of said weighted energies, and leverage said summation to estimate a velocity of the user of the device;
leveraging the weighted energies of the wavelet transformation coefficients to choose the coefficients from which to obtain a reconstructed wave from where each stride of the user is clearly identified;
combining stride time information with the velocity estimation to estimate stride length.
13. The non-transitory processor-readable medium of claim 12 , further comprising:
presentation of an instantaneous value of said velocity on the mobile device in real time.
14. The non-transitory processor-readable medium of claim 13 , further comprising:
dynamic selection of accelerometer sampling frequency and wavelet transformation parameters, comprising levels of decomposition, mother wavelet, and processing time window.
15. The non-transitory processor-readable medium of claim 12 , further comprising:
combination of the user's velocity with other information for enhanced localization and calibration.
16. The non-transitory processor-readable medium of claim 12 , further comprising:
presentation on the mobile device of a moving object whose movement is controlled by the obtained velocity.