this scale is representative of those which appear on outcomes questionnaires and of questions typically asked by a clinician during the routine interview and examination of a patient . for the purpose of this characterization , “ reclining ” is defined as sitting or lying down in such a way so that the lower legs are in a position which is within 45 degrees of horizontal . “ minimal walking ” is defined to be less than 10 steps over a 5 minute period , “ limited walking ” is defined as 10 - 20 steps over a 5 minute period , “ moderate walking ” as 20 - 50 steps over a 5 minute period , “ frequent walking ” as 50 - 100 steps over a 5 minute period and “ very frequent walking ” as greater than 100 steps over a 5 minute period . “ normal average step rate ” is defined to be less than 30 steps / minute and “ high average step rate ” is defined to be greater than 30 steps / minute . similarly “ frequently reclining ” is defined as reclining for greater than 50 % of the time over the 5 minute period , and “ occasionally reclining ” as less than 50 % of the time . activity characterization software module 52 uses the combination of 6000 samples of the flexion and acceleration signals in the x , y and z directions retrieved from data register 28 to differentiate between different types of patient activity in order to characterize the activity of the patient in terms of the activities described above . by using the combination of the retrieved samples of flexion signals and acceleration signals in the x , y and z directions for analysis , activity characterization software module 52 is able to differentiate between activities with validity and accuracy not possible if only one sensor was used . the invention achieves this differentiation by first detecting acceleration cycles in the x direction indicative of steps in the forward direction to identify and characterize periods associated with walking . when the patient takes a step , the acceleration of the lower leg suddenly increases in the forward direction as the patient begins to move the leg forward , then suddenly decreases as the forward motion of the leg slows in order for the foot to make contact with the ground at the end of the step . this activity of taking a step produces a rise and fall in acceleration in the x direction as measured by motion sensor 12 . activity characterization software module 52 operates to detect steps by comparing the magnitude of the ac component of the x direction acceleration signal to a predetermined threshold and incrementing a counter each time the threshold is exceeded . a predetermined latency period of 0 . 5 seconds , during which the counter is disabled after each step is detected prevents multiple counting of a single step after the threshold is first exceeded . by identifying steps over the 5 minute , 6000 sample period , activity characterization software module 52 is able to identify periods within this sample of walking and characterize these periods in terms of step rate . for periods during the sample when no steps are detected as described above , the invention uses the dc components of the measured accelerations in both the x and z directions , indicative of the orientation of motion sensor 12 relative to a gravity reference , to compute the orientation of the limb in order to identify limb positions which are indicative of the patient reclining . during periods when there are no steps and when the orientation of motions sensor 12 as indicated by the dc components of the measured acceleration signals in the x and z directions is found to be greater than 45 degrees from vertical , the patient is determined to be reclining . the combination of these sensors thereby allows the invention to differentiate periods of walking and reclining and thereby characterize the activity of the patient over the 5 minute period in terms of the quantitative scale provided above . if only the signal from a single accelerometer was available , these activities could not be reliably differentiated . while a predetermined time of 5 minutes has been chosen as the interval over which activity levels are computed for comparison and relation to pain , other times may be used . microprocessor 20 executes pain data retrieval software module 48 in response to pain level input detector software module 44 . pain data retrieval software module 48 communicates with data storage software module 36 to retrieve from data register 28 the most recent pain level entered by the patient . as shown in fig4 the pain level retrieved by pain data retrieval software module 48 is combined 54 with characterized activity level computed by activity characterization software module 52 to produce a two element data set of the form ( a , p ) where a corresponds to the level of activity over the 5 minute period immediately preceding entry of a pain level by the patient and p corresponds to the pain level . the levels of the combined data set are communicated to output data storage software module 56 for storage in output register 30 and also communicated to event correlation software module 58 . microprocessor 20 executes event data retrieval software module 50 in response to event input detector software module 42 detecting the entry of an event by the patient . event data retrieval software module 50 communicates with data storage software module 36 to retrieve the value identifying the last event entered by the patient for communication to event correlation software module 58 . event correlation software module 58 maintains an event register . when event correlation software module 58 receives a value identifying an event from event data retrieval software module 50 , event correlation software module 58 stores the received value in the event register for a predetermined time period of 5 minutes . if the event register contains values identifying an events when event correlation software module 58 receives a two element data set of the form ( a , p ) described above and as shown in fig4 event correlation software module 58 produces three element data sets of the form ( a , p , e ) for each value in the event register where a and p are defined as above and e is the value identifying an event entered by the patent in the preceding 5 minutes . as shown in fig4 the three element data sets are communicated to output data storage software module 56 for storage in output data register 30 . the preferred embodiment of the invention is intended to be worn and used by a patient for predetermined time period of seven days in order to produce a series of data sets of the form ( a , p ) and ( a , p , e ) which are stored in output register 30 . alternatively , the invention may be adapted to be worn and used by a patient for longer or shorter time periods or for specified time intervals during a predetermined time period . the data sets stored in output register 30 are indicative of the relationship between pain , characterized activity and specified events over the data collection period . data stored in output register 30 may be retrieved by microprocessor 20 and communicated to an external computer for analysis and display via serial communication interface 32 . the relationship between pain and activity for a given patient is shown can be shown graphically as a matrix . an example graphical matrix is shown in fig5 a . in the matrix depicted in fig5 a , each entry indicates the percentage of the total number of ( a , p ) sets which fall within the boundaries of that element . where there is no entry , no sets fall within that element . the sum of all entries is 100 %. to illustrate , the entry “ 54 %” in the ( activity = 1 , pain = 4 ) element indicates that 54 % of all ( a , p ) sets acquired during the measurement period indicated a pain intensity level of “ 4 ” while the knee was at an activity level of 1 . the graphical matrix shown in fig5 a is representative of a typical patient before undergoing knee joint replacement surgery for treatment of osteoarthritis . the graphical matrix shown in fig5 b is representative of the relationship between pain and activity for the same patient one year after surgery and shows a marked reduction in the level of pain associated with all levels of activity . as shown in fig6 a and 6 b ( a , p , e ) sets can be graphically represented similarly with each matrix displaying information related to the occurrence of a specific event . in fig6 a and 6 b graphical matrixes showing the pain activity relationship are presented for all occurrences of event “ a ” which has been defined as taking a specific pain medication . the graphical matrix shown in fig6 a is representative of the same patient as in fig5 a and 5 b before undergoing knee joint replacement surgery . the graphical matrix shown in fig6 b is representative of the same patient one year after surgery and shows a marked change in the pain / activity relationship associated with this event as a result of treatment . alternatively the graphical matrixes shown in fig5 a , 5 b , 6 a and 6 b can be shown as density graphs in which each element is shaded or patterned in proportion to the corresponding percentage of sample sets which fall within that element . while the preferred embodiment has described the use of the signals from a combination of a flexion sensor and three accelerometers to characterize patient activity in terms of the previously described scale , alternate sensors are utilized in order to differentiate between other types of patient activity . in addition , while the preferred embodiment provides details of sensors applied to characterize physical activity associated with use of the knee joint , the invention may be applied to other joints or to the human body as a whole . such applications include but are not limited to the ankle , hip , wrist , shoulder and spine . for example , fig7 . shows the invention applied to relate neck pain and activity in a patient in order to evaluate the outcome of a treatment for neck ( cervical spine ) injury . in this application flexion sensor 10 is applied to the neck of the patient in order to produce a signal indicative of the flexion of the cervical spine . activity monitoring software module 38 operates in a manner identical to that described previously in order to initiate the execution of pain input request software module 40 . activity characterization software module 52 operates in a manner similar to that described above in order to characterize activity of the patient in terms of a scale consistent with routine clinical practice for evaluating neck injury using the combination of signals from flexion sensor 10 applied at the neck and motion sensor 12 applied at the ankle . in this manner the invention characterizes and differentiates between different types of activity involving use of the cervical spine and produces an indication of the relationship between this activity and pain experienced by the patient .