PATENT DOCUMENT

Publication Number: US-10645991-B2
Application Number: US-201916525875-A
Country: US
Kind Code: B2

Title: Unitless activity assessment and associated methods

Abstract:
A system assesses activity and displays a unitless activity value. A detector senses activity of a user. A processor reads sensed activity data from the detector. A display displays the unitless activity value. An enclosure houses the detector and the processor. The processor periodically reads the sensed activity data from the detector and processes the data to generate an activity number, the number being used to generate the unitless activity value based upon a maximum number and a display range.

Claims:
What is claimed is: 
     
       1. A method of assessing usage by a user of an electronic system comprising a detector and a processor, the method comprising:
 detecting, with the detector, use data indicative of use of the electronic system by the user during a use period; 
 processing, with the processor, the detected use data to determine a number representative of the detected use data during the use period; and 
 generating, with the processor, a value based on a comparison of the determined number and an expected maximum number for the use period. 
 
     
     
       2. The method of  claim 1 , further comprising:
 prior to the generating, identifying a type of activity of the user performed during the use period; and 
 prior to the generating, identifying the expected maximum number for the use period based on the identified type of activity. 
 
     
     
       3. The method of  claim 2 , wherein the identifying the type of activity comprises detecting at least one press of a button. 
     
     
       4. The method of  claim 2 , wherein the identifying the type of activity comprises processing at least a portion of the detected use data. 
     
     
       5. The method of  claim 1 , further comprising presenting the generated value to the user. 
     
     
       6. The method of  claim 5 , further comprising repeating the detecting, the processing, the generating, and the presenting for a plurality of consecutive use periods. 
     
     
       7. The method of  claim 1 , further comprising repeating the detecting, the processing, and the generating for a plurality of consecutive activity periods. 
     
     
       8. The method of  claim 1 , wherein the processing the detected use data to determine the number comprises integrating power spectral density of acceleration data of the detected use data over the period of time of the use period. 
     
     
       9. The method of  claim 1 , wherein the use period is at least eight hours. 
     
     
       10. The method of  claim 1 , wherein the detected use data is indicative of a number of physical repetitions. 
     
     
       11. The method of  claim 1 , wherein the value is based on an intensity of the use of the user during the use period. 
     
     
       12. The method of  claim 1 , wherein the value is based on a length of the use period. 
     
     
       13. The method of  claim 1 , wherein the detected use data is indicative of at least two different activities. 
     
     
       14. The method of  claim 1 , wherein the generating the value comprises:
 determining a ratio based on the comparison of the determined number and the expected maximum number for the use period; and 
 multiplying the determined ratio by a range number. 
 
     
     
       15. A system comprising:
 a detector configured to detect movement data indicative of movement over a movement duration of time; and 
 a processor configured to:
 determine a number representative of the detected movement data over the movement duration of time; and 
 generate a value based on the determined number and a maximum number for the movement duration of time. 
 
 
     
     
       16. The system of  claim 15 , further comprising an enclosure that at least partially houses each one of the detector and the processor. 
     
     
       17. The system of  claim 16 , wherein the enclosure is configured to be worn on a wrist of a user during the movement duration of time. 
     
     
       18. A method of assessing data, comprising:
 detecting data; 
 sampling the detected data during a time period; 
 processing the sampled data to determine a number representative of the sampled data for the time period; 
 adding the number to an accumulator number to provide an updated accumulator number; and 
 determining a value based upon the updated accumulator number and an expected maximum number, wherein the determining the value comprises:
 determining a ratio based on a comparison of the updated accumulator number and the expected maximum number; and 
 multiplying the determined ratio by a range number. 
 
 
     
     
       19. The method of  claim 18 , further comprising presenting the determined value to a user. 
     
     
       20. The method of  claim 18 , wherein the detected data is indicative of a number of physical repetitions.

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 15/972,959 filed May 7, 2018 (now U.S. Pat. No. 10,376,015), which is a continuation of U.S. patent application Ser. No. 15/443,392 filed Feb. 27, 2017 (now U.S. Pat. No. 9,968,158), which is a continuation of U.S. patent application Ser. No. 14/298,454 filed Jun. 6, 2014 (now U.S. Pat. No. 9,578,927), which is a continuation of U.S. patent application Ser. No. 13/544,733 filed Jul. 9, 2012 (now U.S. Pat. No. 8,749,380), which is a continuation of U.S. patent application Ser. No. 13/034,311 filed Feb. 24, 2011 (now U.S. Pat. No. 8,217,788), which is a continuation of U.S. patent application Ser. No. 12/083,726 filed Apr. 16, 2008 (now U.S. Pat. No. 7,911,339), which is a 35 U.S.C. 371 National Phase entry of International Patent Application No. PCT/US2006/040970 filed Oct. 18, 2006, which claims priority to US Provisional Patent Application No. 60/728,031 filed Oct. 18, 2005. All of these earlier applications are incorporated herein by reference. 
    
    
     BACKGROUND 
     Shoes (including sneakers or boots, for example) provide comfort and protection for feet. More importantly, shoes provide physical support for feet to reduce risk of foot injuries. A shoe is often necessary to provide support during intense physical activity, such as running, soccer and American football. As a shoe wears, physical support provided by the shoe decreases, thereby reducing associated protection from injury. When a critical wear level is reached, even if the shoe looks like it is not particularly worn, the shoe may not provide adequate support and may, in fact, cause damage to feet. 
     SUMMARY 
     In one embodiment, a shoe wear out sensor includes at least one detector for sensing a physical metric that changes as a shoe wears out, a processor configured to process the physical metric, over time, to determine if the shoe is worn out, and an alarm for informing a user of the shoe when the sole is worn out. 
     In another embodiment, a system determines the end of a shoe&#39;s life. Use of the shoe is sensed by at least one detector. A processor is configured to measure the use of the shoe and to determine if the shoe is worn out. An alarm informs a user of the shoe when the shoe is worn out. 
     In another embodiment, a body bar sensing system includes a housing with at least one detector for sensing a physical metric that indicates repeated movement of the housing when attached to the body bar, a processor configured to process the physical metric, over time, to determine repetitions thereof, and a display for informing a user of the repetitions. 
     In another embodiment, a system assesses activity and displaying a unitless activity value and includes a detector for sensing activity of a user of the system, a processor for processing sensed activity data from the detector, a display for displaying the unitless activity value, and an enclosure for housing the detector and the processor. The processor periodically reads the sensed activity data from the detector and processes the data to generate an activity number, the number being used to generate the unitless activity value based upon a maximum number and a display range. 
     In another embodiment, a method determines a unitless activity value for a desired period of activity. A period accumulator is cleared prior to the start of the activity period. A detector is periodically sampled to obtain data that is processed to determine a number representative of the sampling period. The number is added to the period accumulator. The unitless activity value is then determined based upon the period accumulator, a maximum activity number and a display range. The unitless activity value is then displayed. The sampling, processing and adding are repeated until data is sampled for the desired period of activity. 
     In another embodiment, a method assesses activity unitlessly by detecting motion of a user, processing the detected motion, over time, to determine an activity value, ratioing the activity value to a maximum activity value, and reporting a scaled unitless activity value to the user based upon the ratio and a scale. 
     A software product has instructions, stored on computer-readable media, that, when executed by a computer, perform steps for determining a unitless activity value for a desired period of activity, including instructions for: detecting motion of a user, processing detected motion, over time, to determine an activity value, ratioing the activity value to a maximum activity value, and reporting a scaled unitless activity value to the user based upon the ratio and a scale. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  shows one exemplary embodiment of a shoe wear-out sensor. 
         FIG. 2  shows one exemplary embodiment of a shoe with a shoe wear out sensor. 
         FIG. 3  shows another exemplary embodiment of a shoe with a shoe wear out sensor. 
         FIG. 4A  shows one exemplary process for determining shoe wear out. 
         FIG. 4B  shown one exemplary process for determining shoe wear out. 
         FIG. 4C  shows one exemplary process for determining shoe wear out. 
         FIG. 4D  shown one exemplary process for determining shoe wear out. 
         FIG. 5  shows one body bar sensing system embodiment. 
         FIG. 6  shows one part of an exemplary body bar with a body bar sensing system embodiment attached. 
         FIG. 7  shows one part of a body bar in an embodiment showing a weight and a body bar sensing system that secures the weight onto the body bar. 
         FIG. 8  shows one exemplary process for reporting body bar usage. 
         FIG. 9  shows an embodiment of a sensor that unitlessly assesses activity. 
         FIG. 10  shows a process for unitlessly determining activity. 
     
    
    
     DETAILED DESCRIPTION OF THE FIGURES 
       FIG. 1  shows one shoe-wear out sensor  100 . Sensor  100  includes a processor  102 , a detector  104  and an alarm  106 . A battery  108  may be used to power processor  102 , detector  104  and alarm  106 ; alternatively, a magnetic coil generator (not shown) or other mechanical motion-to-electricity conversion device may be employed with sensor  100  to power these elements. Detector  104  is for example an accelerometer and/or a force sensing resistor (FSR). Alarm  106  is for example a light emitting diode (LED) and/or a small speaker and/or a small sound actuator (e.g., a buzzer, piezoelectric beeper etc). 
       FIG. 2  shows a shoe  200  with a shoe-wear out sensor  210 . Shoe  200  is for example a running or sport shoe, boot (e.g., a snowboard or hiking boot), slipper, dress shoe or flip-flop; shoe  200  may alternatively be an orthopedic shoe for providing special foot support. Sensor  210  may represent sensor  100 ,  FIG. 1 . In the illustrated embodiment, shoe  200  has a sole  202  and an upper part  204 . Sole  202  has an outsole  206  and a heel  208 . Sensor  210  is shown contained within heel  208 ; however sensor  210  may be placed elsewhere within or on the shoe to function similarly. 
       FIG. 3  shows one exemplary embodiment of a shoe with a shoe-wear out sensor  310 . Sensor  310  may again represent sensor  100 ,  FIG. 1 . Shoe  300  is shown with a sole  302  and an upper part  304 . Sole  302  has an outsole  306  and a heel  308 . Shoe  300  may again represent, for example, a running shoe, sports shoe or orthopedic shoe (or other type of shoe or boot). Electronics  310   a  of sensor  310  are shown contained within heel  308 ; but detector  312  is shown located within outer sole  306 , illustrating that the elements of sensor  100  ( FIG. 1 ) may be dispersed to various locations of the shoe while providing similar functionality. Detector  312  is for example detector  104 ,  FIG. 1 ; it may thereby be a force sensing resistor and/or a piezoelectric foil that is electrically connected, via connection  314 , to electronics  310   a  of sensor  310 . If detector  312  is a piezoelectric foil (or other piezoelectric device), use of shoe  300  results in flexing of detector  312  which may generate sufficient electricity to power electronics  310   a  of sensor  310 , avoiding the need for battery  108 . 
       FIGS. 1, 2 and 3  are best viewed together with the following description. Sensor  100  may be embedded in a shoe (e.g., sensors  210 ,  310  within shoes  200 ,  300 ) and configured to determine when that shoe has “worn out”. It then informs the user, via alarm  106 , that it is time to buy a new shoe (usually a new pair of shoes). In an embodiment, alarm  106  is an LED  217  that is positioned at the outside of the shoe such that it may be seen, when activated, by the user of the shoe, as illustratively shown in  FIG. 2 . 
     Processor  102  may operate under control of algorithmic software  103  (which is illustratively shown within processor  102 , though it may reside elsewhere within sensor  100 , for example as stand alone memory of sensor  100 ). Algorithmic software  103  for example includes algorithms for processing data from detector  104  to determine when a shoe is worn out. 
       FIG. 4A  for example illustrates one process  400  performed by processor  102  of  FIG. 1 . In step  402 , processor  102  samples detector  104  to determine a physical metric associated with the shoe. In an example of step  402 , detector  104  is an accelerometer and thereby provides acceleration data resulting from movement of the shoe upon a surface as the physical metric. For example, as the shoe strikes the ground when in use, processor  102  takes a plurality of samples using detector  104  to form an impact profile. In step  404 , processor  102  processes the physical metric and compares it against a predetermined threshold, response curve or other data reference. In an example of step  404 , processor  102  compares the impact profile determined from the accelerometer against an impact profile of a “new” shoe. In another example of steps  402 ,  404 , the physical metric is power spectral density corresponding to certain frequencies of interest; and the power spectral density is compared, during use of the shoe, to a data reference containing power spectral density of a new or acceptably performing shoe. If the current data (i.e., physical metric) is too large or exceeds the data reference, for example, then processor  102  sets off alarm  106  (e.g., lights LED  217 ) in step  406 . In one embodiment, upon first use of the shoe, processor  102  determines an impact profile of the new shoe that is then used (e.g., as the threshold or data reference) in comparison against subsequently determined impact profiles. Or, upon first use of the shoe, for example, processor  102  may store the appropriate data reference (e.g., power spectral density or threshold) for comparison against data captured in latter uses of the shoe. In this way, therefore, process  400  may be efficiently used to inform a user of shoe wear out. 
     As noted, data from detector  104  may be processed in the frequency domain (e.g., using Fourier transforms of data from detector  104 ) so as to evaluate, for example, power spectral density of the physical metric (e.g., acceleration or force), in step  404 . In this manner, therefore, a range of frequencies may be evaluated (e.g., an area under the curve for certain frequencies may be integrated) from detector  104  and then compared to similar data (as the threshold) of a new shoe. As a shoe wears, the elasticity of the material from which it is made changes; thus the ability of the material to absorb the shock of the shoe contacting the ground deteriorates, resulting in more shock force being transferred to the foot within the shoe. By determining the increase of the shock force above the threshold, in this embodiment, the wear on the shoe may be determined. 
     We now specifically incorporate by reference the teachings and disclosure of: U.S. Pat. Nos. 6,539,336; 6,266,623; 6,885,971; 6,856,934; 6,8963,818; 6,499,000; and 8,280,682. These patents provide useful background, power sensing and weight/movement monitoring techniques suitable for use with the teachings of this present application. 
     In an embodiment, similar to the embodiment of  FIG. 3 , processor  102  determines wear of shoe  300  based upon weight of the user of shoe  300 . By using signals from detector  312  to determine an approximate weight of the user of shoe  300  (for example by using a pressure sensor and fluid-filled cavity as detector  104 ), processor  102  may determine a life expectancy of shoe  300 . Since the wear on the shoe is roughly proportional to the weight applied by the wearer, during activity, by determining the weight of the wearer and the amount the shoe is used (e.g., how often and how long the shoe is used), processor  102  may thus determine shoe wear with increased accuracy. That is, a shoe used by someone who spends most of their time sitting at a desk receives less wear that a shoe used by someone who spends most of the day standing on their feet. 
     In another embodiment, by sensing when the shoe is used—or for how long—the teachings herein may instead be applied so as to set off the alarm after a term or time of use has expired. For example, if a shoe is specified for use to at least 100 hours or 500 miles (or other similar metric specified by the shoe manufacturer), then by sensing weight or acceleration (or other physical metric, via detector  104 ) that use may be determined; processor  102  then activates alarm  106  when the use is exceeded. For example, using one or more accelerometers as detector  104 , speed of the shoe may be determined through operation of processor  102  using an appropriate algorithm within software  103 ; this processor  102  then uses the speed information to determine distance traveled and sets off alarm  106  when, for example, the manufacturer&#39;s specified distance use is met. Illustratively, in another example, if the manufacturer specifies that the shoe may be used under normal conditions for 500 hours (or some other time), then detector  104  in the form of an accelerometer may determine when the shoe is in use; processor  102  then determines the period of use, over time (e.g., weeks and months) and sets off alarm  106  when the accumulated use exceeds the specified limit. 
       FIG. 4B  for example illustrates one process  450  performed by processor  102  of  FIG. 1  for determining shoe wear out. In step  452 , processor  102  samples detector  104  to determine one or more physical metrics associated with the shoe. In an example of step  402 , detector  104  includes a fluid filled cavity and a pressure sensor and thereby provides a signal representative of force upon the shoe (e.g., a value representative of the weight of the user of the shoe). For example, as the shoe is used, processor  102  takes a plurality of pressure reading from detector  104 . In step  454 , processor  102  determines an approximate weight upon the shoe based upon samples of step  452 . In one example of step  454 , processor  102  utilizes algorithms of software  103  to determine an approximate weight of the user of the shoe based upon pressure values sensed by detector  104 . In step  456 , processor  102  determines the duration of the shoe&#39;s use. In one example of step  456 , processor  102  utilizes algorithms of software  103  to measure the duration that the shoe is used based upon readings from detector  104  and an internal timer of processor  102 . In step  458 , processor  102  determines the shoe use for the sample period of step  452 . In one example of step  458 , processor utilizes algorithms of software  103  to determine a use factor based upon the determined weight of step  454  and the duration of use of step  458 . In step  460 , processor  102  determines remaining life of the shoe based upon the determined shoe use of step  458 . In one example of step  460 , processor  102  maintains a cumulative value of usage determined in step  458  for comparison against a manufacturer&#39;s expected usage of the shoe. In step  462 , processor  102  enables alarm  106  if the shoe&#39;s life is exceeded. Steps  452  through  462  repeat periodically throughout the life of the shoe to monitor shoe usage based upon wear determined from the weight of the user and the duration of use. 
     In the above description of process  450 , it is not necessary that weight be determined. Rather, in an embodiment, it may instead be determined that the shoe is in “use” based on an algorithm using the pressure or force based detector  104 ; and then this use is accumulated time-wise to determine when the shoe&#39;s life expectancy is exceeded. For example, once a user puts weight onto this detector (in this embodiment), then processor  102  detects (through use of an algorithm as software  103 ) that the shoe is in use due to the presence of weight onto detector  104 . 
       FIG. 4C  for example illustrates one process  470  performed by processor  102  of  FIG. 1  for determining shoe wear out. In step  471 , processor  102  samples detector  104  periodically over a defined period. In one example of step  471 , detector  104  is an accelerometer that is sampled periodically by processor  102  over a period often seconds. In step  472 , processor  102  determines if the shoe is in use. In one example of step  472 , processor  102  utilizes algorithms of software  103  to process the samples of step  471  to determine if the shoe is in use. Step  473  is a decision. If, in step  473 , processor  102  determines that the shoe is in use, process  470  continues with step  474 ; otherwise process  470  continues with step  475 . In step  474 , processor  102  adds a value representative of the defined period of step  471  to an accumulator. In one example of step  474 , a non-volatile accumulator is incremented by one, where the one represents a period often seconds. Step  475  is a decision. If, in step  475 , processor  102  determines that the shoe is worn out, process  470  continues with step  476 ; otherwise process  470  continues with step  471 . In one example of the decision of step  475 , processor  102  compares the use accumulator of step  474  against a value representative of the expected life of the shoe. Steps  471  through  475  repeat throughout the lifetime of the shoe. As appreciated, power saving measures may be used within sensor  100  when it is determined that the shoe in which sensor  100  is installed is not in use. In step  476 , processor  102  enables alarm  106 . In one example of step  476 , processor  102  may periodically activate LED  217 ,  FIG. 2 , until battery  108  is exhausted. 
     Process  470  thus determines the wear on a shoe by measuring the amount of use and comparing it against the expected use defined by a manufacturer, for example. In an embodiment, the use accumulator of step  474  is a timer within processor  102 . This timer is started when step  473  determines that the shoe is in use and is stopped when step  473  determines that the shoe is not in use. This timer thus accumulates, in real time, the use of the shoe for comparison against a manufacturer&#39;s expected use. In another embodiment, step  472  may determine the number of steps a shoe has taken such that the use accumulator of step  474  accumulates the total number of steps taken by the shoe. This total number of steps is then compared to the manufacturer&#39;s recommended number of steps expected in the shoes life time. 
       FIG. 4D  illustrates one process  480  performed by processor  102  of  FIG. 1  for determining shoe wear out. In step  481 , processor  102  samples detector  104  periodically over a defined period. In one example of step  481 , detector  104  is an accelerometer and processor  102  samples acceleration values over a period of 1 second. In step  482 , processor  102  determines if the shoe is in use. In one example of step  482 , processor  102  utilizes algorithms of software  103  to determine if characteristics of samples values of step  481  indicate that the shoe is in use. Step  483  is a decision. If, in step  483 , processor  102  determines that the shoe is in use, process  480  continues with step  484 ; otherwise process  480  continues with step  486 . In step  484 , processor  102  determines a distance traveled over the defined period of step  481 . In one example of step  484 , processor  102  utilizes algorithms of software  103  to first determine speed of the shoe, and then determines distance covered in one second. In step  485 , processor  102  accumulates the distance traveled. In one example of step  485 , processor  102  adds the distance determined in step  484  to a total distance traveled accumulator. In one example, this accumulator is stored in non-volatile memory. Step  486  is a decision. If, in step  486 , processor  102  determines that the shoe is worn out, process  480  continues with step  487 ; otherwise process  480  continues with step  481 . In one example of step  486 , processor  102  compares the total accumulated distance of step  485  against the manufacturer&#39;s recommended maximum distance for the shoe. Steps  481  through  486  repeat throughout the lifetime of the shoe. As appreciated, power saving measures may be used within sensor  100  when it is determined that the shoe is not in use. In step  487 , processor  102  enables alarm  106 . In one example of step  487 , processor  102  may periodically activate LED  217 ,  FIG. 2 , until battery  108  is exhausted. Process  480  thus determines shoe wear by measuring the distance traveled by the shoe, using one or more accelerometers, and compares that distance to a manufacturer&#39;s recommended maximum distance for the shoe. 
       FIG. 5  shows a body bar sensing system  500 . System  500  includes a housing  502 , a processor  504 , a detector  506  and either an internal display  508  or an external display  512 . A battery  510  may be used to power processor  504 , detector  506  and display  508 / 512 . Detector  506  is for example an accelerometer or a Hall Effect sensor. Display  508 / 512  is for example a liquid crystal display and/or a small speaker (e.g., that emits voice annunciations or other sounds generated by processor  504 ). 
       FIG. 6  shows one part of an exemplary body bar  602  with body bar sensing system  500  attached; a weight  604  and a retaining clip  606  are also shown to secure weight  604  onto body bar  602  (note, some body bars use no weights but weight is shown in  FIG. 6  for illustrative purposes). Body bar  602  may represent a work out bar used by people in the gym, or a barbell, or other similar apparatus that requires a number of repetitions in exercise.  FIG. 7  shows body bar  602  in an embodiment with another body bar sensing system  500  that secures weight  604  onto body bar  602 . That is, sensing system  500  in addition operates as retaining clip  606 ,  FIG. 6 . 
       FIGS. 5, 6 and 7  are best viewed together with the following description. Housing  502  attaches to body bar  602  as shown in  FIG. 6  or as shown in  FIG. 7 . Processor  504  utilizes detector  506  to determine when system  500  (as attached to body bar  602 ) has performed one repetition; it then informs the user, via display  508 / 512  for example, of a number of repetitions (or whether the user has performed the right number or any other number of planned repetitions as programmed into processor  504 ). 
     Where display  512  is used (i.e., remote from housing  502 ), a wireless transmitter (not shown) may be included within housing  502  to remotely provide data from processor  504  to remote display  512  (as shown in dotted outline). Where display  508  is integral with housing  502 , then display  508  provides a visual display for a user when housing  502  attaches to the body bar. In one embodiment, display  512  (shown in dotted outline) is part of a watch (or a MP3 player or a cell phone) that may be seen when worn or used by the user when performing exercises; and measurements determined by processor  504  are transmitted to the watch (or to the MP3 player or cell phone) for display upon display  512 . 
     Processor  504  may operate under control of algorithmic software  505  (which is illustratively shown within processor  504  although it may reside elsewhere within housing  502 , such as stand alone memory within housing  502 ). Algorithmic software  505  for example includes algorithms for processing data from detector  506  to determine the repetitions performed by a user of body bar  602 . 
       FIG. 8  shows one exemplary process  800  performed by processor  504 . In step  802 , detector  506  samples a physical metric associated with body bar  602 . In an example of step  802 , detector  506  is an accelerometer and thereby provides acceleration as the physical metric. In another example of step  802 , detector  506  is a Hall effect sensor which detects inversion (and thus repetition) of bar  602 . In step  804 , processor  504  processes the physical metric to assess whether the metric indicates a repetition of body bar  602 . In an example of step  804 , processor  504  evaluates the acceleration to determine if body bar  602  has been raised or lowered within a certain time interval. In step  806 , repetition information is displayed to the user. In an example of step  806 , the number of repetitions is relayed remotely (wirelessly) to a watch that includes display  512 . That watch may also include a processor to store data and inform the user of repetitions for workouts, over time. 
       FIG. 9  shows one exemplary system  900  for unitlessly assessing activity of a user. System  900  has a processor  904 , a detector  906  and a battery  908  within an enclosure  902  (e.g., a plastic housing). System  900  may include a display  910  for displaying unitless units to the user. Alternatively (or in addition), a remote display  912  is used to display the unitless units; in this case, enclosure  902  includes a wireless transmitter  913  in communication with, and controlled by, processor  904 , so that transmitted unitless assessment numbers are sent to remote display  912 . 
     In an embodiment, detector  906  is an accelerometer and processor  904  determines a value representing an activity level of the user of system  900  for display on display  910  or display  912 . The accelerometer is for example positioned within housing  902  so that, when housing  902  is attached to a user, accelerometer  906  senses motion perpendicular to a surface (e.g., ground or a road or a floor) upon which the user moves (e.g., runs, dances, bounces). Data from the accelerometer is for example processed in the frequency domain as power spectral density (e.g., by frequency binning of the data). Multiple accelerometers (e.g., a triaxial accelerometer) may also be used as detector  906 —for example to sense motion in other axes in addition to one perpendicular to the surface—and then processed together (e.g., in power spectral density domain) to arrive at a unitless value (as described below). 
     Processor  904  may utilize one or more algorithms, shown as software  905  within processor  904 , for processing information obtained from detector  906  to assess the activity of the user. For example, processor  904  may periodically sample detector  906  to measure acceleration forces experienced by the user (when enclosure  902  is attached to the user, e.g., at the user&#39;s belt or shoe). Processor  904  may then process these forces to assess the activity level of the user. This activity level may represent effort exerted by the user when skiing. 
     The following represents a typical use of system  900 , in an embodiment. In this example, detector  906  is one or more accelerometers. First, processor  904  determines when system  900  is in use, for example by sensing movement of housing  902  that corresponds to known activity (e.g., skiing or running). Alternatively, system  900  includes a button  915  that starts processing (in which case, separate determination of a known activity is not necessary). In an embodiment, button  915  is located proximate to display  912 , and communicated wirelessly with processor  904 . In this case, wireless transmitter  913  is a transceiver and button  915  includes a transmitter or a transceiver. 
     Once processor  904  knows (by sensing motion) or is notified (by button  915 ) that system  900  is operating in the desired activity, then it collects data over a period of that activity—for example over 1 hour (a typical aerobic hour), 4 hours (a typical long run), 8 hours (a typical “ski” day) or over one full day, each of these being typical sport activity periods; however any time may be used and/or programmed in system  900 . In an example, processor  904  integrates power spectral density of acceleration over this period of time to generate a number. This number in fact is a function of g&#39;s, frequency units and time, which does not make intuitive sense to the user. For example, consider a professional athlete who snowboards down difficult, double diamond terrain for eight hours. When system  900  measures his activity over this period, his number will be high (e.g., 500 “units” of power spectral density) because of his extreme physical capabilities. Then, when a less capable user uses system  900 , a number of, e.g., 250 units may be generated because the user is not as capable (physically and skilled) as the professional. Therefore, in this example, an expected maximum number, shown as MAX  914  within processor  904 , may be set at 500. A display range, shown as RNG  916  within processor  904 , may also be defined such that system  900  may display a unitless value that is relative to the maximum number. Continuing with the above example, if RNG  916  is set to 100, system  900  displays a unitless value of 100 for the professional athlete and a unitless value of 50 for the less capable user (i.e., the less capable user has a 50% value of the professional athlete). By setting RNG  916  to other values, the displayed output range of system  900  may be modified. 
     In one example of use, system  900  is formed as a wrist watch to facilitate attachment to a child&#39;s wrist. System  900 , when worn by the child, may then determine the child&#39;s activity level for the day. In another example of use, system  900  may be attached to a person&#39;s limb that is recuperating from injury (e.g., sporting injury, accident and/or operation etc.) such that system  900  may determine if the limb is receiving the right amount of activity to expedite recovery. 
     In another example of use, two skiers each use a system  900  when skiing for a day. The first skier, who is experienced and athletic, skis difficult ski runs (e.g., black double diamonds) all day, whereas the second skier is less experienced and skis easy runs (e.g., green runs) all day. At the end of the day, the first skier has a unitless activity value of 87 and the second skier has a unitless activity value of 12. Thus, these unitless activity values indicate the relative activity levels of each skier. 
       FIG. 10  shows a flowchart illustrating one process  1000  for determining and displaying a unitless value representative of a user&#39;s activity. Process  1000  may represent algorithms within software  905  of  FIG. 9 , for example, to be executed by processor  904 . In step  1002 , process  1000  clears a period accumulator. In one example of step  1002 , processor  904 , under control of software  905 , clears period accumulator  918 . In step  1004 , process  1000  samples the detector to obtain data. In one example of step  1004 , processor  904  periodically samples detector  906  over a sample period to determine data representative of the user&#39;s activity for that period. In step  1006 , process  1000  processes the data of step  1004  to determine a number. In one example of step  1006 , processor  904  integrates power spectral density of acceleration sampled in step  1004  over the sample period of step  1004  to generate a number. In step  1008 , the number determined in step  1006  is added to the period accumulator. In one example of step  1008 , processor  904  adds the number determined in step  1006  to period accumulator  918 . In step  1010 , process  1000  determines a unitless activity value from the accumulator. In one example of step  1010 , processor  904  converts the accumulated value to a display value based upon MAX  914  and RNG  916 . In step  1012 , process  1000  displays the determined unitless activity value. In one example of step  1012 , processor  904  sends the determined unitless activity value to display  912  via wireless transmitter  913 . Step  1014  is a decision. If, in step  1014 , the activity period for display has ended, process  1000  terminates; otherwise process  1000  continues with step  1004 . Steps  1004  through  1014  thus repeat until the desired activity period is over. 
     Changes may be made to this application without departing from the scope hereof. It should thus be noted that the matter contained in the above description or shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. The following claims are intended to cover all generic and specific features described herein, as well as all statements of the scope of the present method and system, which, as a matter of language, might be said to fall there between.

Metadata:
Filing Date: 20190730
Publication Date: 20200512
Grant Date: 20200512
Priority Date: 20051018
Inventors: VOCK, CURTIS A.
YOUNGS, PERRY
Assignee: APPLE INC
CPC Classifications: [{"code": "A43B1/0036", "inventive": true, "first": false, "tree": "[]"}, {"code": "G08B21/182", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B7/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "A43D1/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B1/0036", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B7/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G01N33/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G08B7/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/0021", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/0005", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01N2033/0086", "inventive": false, "first": false, "tree": "[]"}, {"code": "G01N33/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43D1/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B1/0036", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B7/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G01N2033/008", "inventive": false, "first": false, "tree": "[]"}, {"code": "G08B7/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G08B21/182", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/34", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/34", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "A43B3/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01N33/008", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01N33/0086", "inventive": true, "first": false, "tree": "[]"}, {"code": "G01N33/008", "inventive": false, "first": false, "tree": "[]"}, {"code": "G01N33/0086", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 37708410