- vertical hip motion range: The marker mounted to the hip is tracked for up and down range of motion, where ranges which are too small or too large suggest that the saddle height is not properly adjusted. It can also indicate that a different saddle altogether is appropriate.
- wrist motion range: The marker mounted to the wrist is tracked for any motions that stray significantly from a single point. If the wrist is moving during cycling, it indicates that the arms of the cyclist are over-extended or under-extended. Either of these conditions suggests that the handlebars are not in an optimum position for the cyclist, and should be adjusted or replaced.
- lateral knee motion range: The marker on the knee should describe a repetitive nearly planar pattern. Deviations from a plane indicate that the knee is swinging outward or inward from a “same-plane” position and that adjustment is needed (such as wedges between the shoe and the pedal).
- ankle motion range: Similarly, unexpected inward or outward movement (toward or away from the frame) with respect to the foot indicates that wedges or spacers are advisable.
- foot motion range: The foot marker should describe a nearly perfect circle; the foot should be pedaling pedals that themselves are traveling in a circular motion. Any deviation from nearly a planar circle suggests that foot wedges or other adjustments are needed.
- ankle-knee-hip angle range: Mean and extrema values for the knee angle range which are too large indicate that the saddle is too high; values which are too small indicate that the saddle is too low.
- mean knee-hip-shoulder angle: The mean value of this angle relates to the goal of the cyclist: small angle for speed, large for comfort; the invention might suggest certain angular ranges corresponding to various goals based on historically accumulated data.
- relative forward-aft location of knee and pedal: Non-zero forward-aft horizontal displacement of the knee marker with respect to the foot marker indicates that the saddle is too far forward or rearward.

Once a bicycle fitting session has been completed, and all variations in motion of the cyclist on the bicycle are found to be within the proper limits, the data from that session is added to a master database of successful fit sessions. For privacy reasons, the names (addresses, phone numbers, . . . ) of the cyclists would not be recorded therein. Hence, the more fitting sessions that are performed, the more honed the recommendation process becomes. That is, rather than using fixed, predetermined rules for making recommendations, the system can “learn” as it is updated with additions and modifications to the master database. Data from multiple systems can be pooled, analyzed, and shared to more quickly build quantitative ranges describing good versus bad fits for individuals of various body geometries. Session data from recognized expert fitters might be weighted more heavily.

Because cyclists differ substantially in size, some thresholds, limits, and recommendations should be scaled relative to distances between markers, which will presumably be proportional to the size of (the relevant body parts of) the subject cyclist. For example, the upper limit for inward-outward motion of the knee should be proportional to the leg size, which will roughly be proportional to the ankle-to-knee distance plus ankle-to-hip distance.

Once all data is gathered, it is analyzed for potential adjustments to bicycle components, substitution of different bicycle components, or even substitution of a different bicycle or bicycle frame. Without re-harnessing the cyclist, one or more acquisition sessions are repeated after the adjustments until the desired quality fit is achieved. Each of the measured statistical values can be re-measured and evaluated individually to optimize each aspect of the bicycle fit.

The detailed procedural steps for the preferred embodiment of the present invention are listed inFIGS. 2 and 3.FIG. 2 lists the steps that the operator of the system generally would perform manually (although thesoftware34 might at least prompt the operator for each step to perform and prompt the operator to input session and cyclist parameters).FIG. 3 lists the automated steps that would normally be implemented in or controlled by the bicyclefitting application software34 running oncomputer30. The division of tasks between the operator (FIG. 2) and the invention hardware and software (FIG. 3) is not absolute; an advanced system might do more steps automatically.

InFIG. 2, the method begins withstep101, which attaches detectable markers to at least one side of the cyclist (rider, person) and optionally to the bicycle (or other vehicle or conveyance being powered by the person). If additional markers are also attached to the bicycle, those markers can serve to define a coordinate system relative to the bicycle rather than relative to the tracker which detects and measures the locations of the markers. In either case,step102 defines the axes of a three-dimensional coordinate system.

Step

103, which checks the orientation of the saddle or seat may be either manual step or may use a probe tracked by the tracking system to automate partially the check. This might also involve noting the style and width of the saddle. This check would be ignored or would use a very different criterion for a recumbent bicycle.

Step104 acquires details and characteristics about the rider (cyclist) and the bicycle (or other conveyance). This would presumably include at least the name of the rider so that saved information can later be retrieved by name. Other pertinent information might include the cyclist's gender, age, experience (from beginner to racer) and goal (comfort, endurance, or performance efficiency). Other relevant information would characterize the bicycle. Because a 3-dimensional tracking system can also track markers on a probe, it is possible to arrange for such a probe to measure certain landmark locations on the bicycle such as the axis of the pedals, the top of the seat, wheel centers, points along the top tube or handlebars, and the like. More preferably the computer system would have a database of bicycle models and their geometry, so that once the bicycle model is identified, all its geometry would be available.

After directing the cyclist to begin powering the bicycle (step105), which would normally be well supported in a stationary but otherwise normal mode of operation, step106 would invoke the computer system to begin executingsteps201 through208 inFIG. 3. (Note that the computer system may already be prompting the system's human operator to perform the manual steps of the method: namely, those listed inFIG. 2.) Step201 sets up a marker tracking system to acquire a sequence of discrete locations at discrete times for each marker for a period of time. Step202 then actually acquires and saves the sequence of locations for each marker. Along with the spatial coordinates are saved the time of acquisition, the success or failure of acquisition, and some association between markers and their corresponding temporal-spatial coordinates, such as an index number or mnemonic identifier. The acquisition time might be as short as a few seconds to as long as many minutes, but should include at least a number of sample locations over at least one cycle of the motion which powers the bicycle or other vehicle (such as at least one revolution of pedals or the complete cycle of a rowing stroke).

Optional step

203 transforms the coordinates to a more computationally convenient or standard coordinate space, such as a coordinate system with the origin at the center of the circle approximately traced by the foot marker and one with, for example, the +Z coordinate axis pointing up and the +X axis pointing forward. Similarly, the time coordinates may be adjusted to be relative to the beginning of the sequence of time coordinates.

In order to summarize the location information about each marker, step204 computes statistics about each marker. This likely would include at least its mean XYZ location (centroid) and range of motion along each of the X, Y, and Z axes (corresponding perhaps to forward-aft motion, lateral motion, and vertical motion).

Still in the same vein, step205 would compute at least one angle formed by a specified trio of markers. One obvious example is the angle of the bent leg at the knee and how it relates to proper saddle height. If the maximum angle is too near 180 degrees the seat is too high; if it is too small, the seat is too low. The leg angle is approximated by the angle formed by the ankle-knee-hip trio of markers at some moment. The mean value of this angle is probably not as interesting as the maximum angle.

Once sufficient discrete locations are acquired for each marker, each marker's sequence of locations may be fit to a continuous trajectory which approximates the actual path of the marker. This may be as simple as a sequence of connected straight line segments or may be represented by smooth curves such as cubic-polynomial splines. Although not absolutely necessary, there are two reasons for fitting the discrete locations to a continuous trajectory: (a) for a displaying visually pleasing graphical presentation of the marker paths to the system operator and (b) to provide the capability to interpolate locations of a marker at times between the times of two or more known, nearby acquired locations. The latter is particularly important if the location of only one marker is determined at a time. Interpolation allows the system to estimate the position of some or all of the markers at the same instant in time even though the locations of the markers were acquired at slightly different times.

Furthermore, the actual trajectory might be matched with or compared to an ideal or standardized trajectory or to all historically recorded marker trajectories of the same cyclist.

Step206 ofFIG. 3 allows for the optional visual depiction at least some of the acquired and computed data overlaid onto a stylized graphical representation of the person (cyclist) and vehicle (bicycle). Alternatively, as shown inFIG. 4, the data may be in a tabular form with reference identifiers associating each datum with the distance or angle identified on the graphical representation of the cyclist. Step207 further provides for a permanent printed or electronic report that the can be kept for future reference or given to the person. This may include the original acquired coordinates or simply the computed results and recommend changes or both. Similarly, the computed results (angles, distances, their ratios, and like statistical results) can be compared with norms or with results from previous sessions (step208).

The computer software would compare the results of the current session with a database of norms, measurement ranges, and other criteria, where the database is built from experts' advice and from collected results of successful fittings. The software would match the present session results with entries in the database. Associated with entries in the database would be recommended alterations to the bicycle (or other vehicle) or its components. Alternatively, for somebody desiring to purchase a bicycle, the database would list possible models, frame sizes, and changeable components which best match the present results. This could be implemented using common database software. Obtaining the data content for the database is the bigger practical challenge.

Because all fitting data is available on the computer system, over time it is available for compiling into statistical norms which would form a growing and improving content for the database. Data from many installations of the invention would be centrally compiled and shared. It would be important to attach to each cyclist's data file a rating indicating how satisfactory the cyclist found the fit to be. Ideally this assessment would be made after a period of real-world experience riding the bicycle.

Once the computer system has acquired motion data, processed it into meaningful summary statistics, and supplied database-oriented recommendations, the level of satisfaction is determined in step109 ofFIG. 2. If the statistics are within norms that match the cyclist's goals (comfort, endurance, efficiency or the like) and if the cyclist is satisfied with the quality of fit with the bicycle the session may be completed simply by saving acquired data and/or computed results in a form for possible future reference. The results would then be used to design a custom bike, finalize purchase of the bike used, finalize purchase of the new components used, or aid in some other decision for which the invention was used to quantify the quality of fit.

If data was acquired from only one side of the cyclist, it might be desirable to acquire data from the other side and compare the results for equality or symmetry, for example. This could be done whether or not the results suggested that the cyclist-to-bicycle interface (fit) was satisfactory. The latter suggests that the computer software be capable of overlaying the results for both sides for visual comparison—this in addition to the obvious item-for-item comparison of the numeric results. Seestep108 ofFIG. 2.

However, if the last report of results was unsatisfactory or the computer software recommended a substitution or a bicycle-specific alteration to the configuration, the cyclist or operator can choose whether to comply. See step107 ofFIG. 2. After making the change, the whole method would be reapplied—presumably returning to step105 (if not an earlier step). The alteration would be tailored to the cyclist's goal (comfort, endurance, maximal power output, or so on) and skill level (beginner to professional). Potential alterations (besides substitution of a wholly different frame or bicycle) could include any of the following: longer or shorter pedal crank arms, pedal spacers or wedges, seat width, seat height, seat angle, seat fore/aft position, handlebar height, handlebar stem length, handlebar style, and handlebar angle. If the cyclist is riding a bicycle simulator instead of a normal bicycle, frame geometry and dimensions can also be adjusted (manually or perhaps even automatically under computer control)—as if a new bicycle was being substituted.