parkinson 's disease ( pd ) is a degenerative disease characterized by depletion of dopamine , which leads to the development of akinesia ( poverty in movements generation ) and bradykinesia ( slowness in executed movements ) [ 15 ] . these motor symptoms should be examined in daily life , since routine neurological examination in the hospital can detect only certain but not all aspects of motor abnormalities . long - term monitoring devices have been developed to analyze motor deficits that interfere with daily activities [ 610 ] . we also developed recently the portable gait rhythmogram ( pgr , mgm-1100 ) that allows exact extraction of gait - related accelerations from overall motion - related accelerations during long - term monitoring [ 11 , 12 ] . the  pattern matching method  is mathematically used to identify gait - related acceleration . using the pgr , we have already described poverty of movements and deviation in gait rhythm , such as freezing of gait [ 11 , 12 ] , and addressed motor fluctuations by tracing these parameters . previous studies indicated that pd patients can only execute movements of different amplitudes at a single and slow velocity and can not increase their movement velocity . another study reported limited peak emg activity that can be generated in a muscle burst in pd patients . to examine the hypothesis of  deficit in scaling  on gait , it is necessary to ask subjects to walk at various rhythms or powers and to quantify the changes in each gait parameter . although this analysis is experimentally difficult , one potentially useful approach would be to examine data recorded during daily activities since in daily walking , subjects are obliged to vary gait strategies depending on the context , for example , easy walking or strenuous walking . daily long walk could be a good sample for examining the range of changes in gait parameters . using our newly developed pgr , we tested the hypothesis that pd patients find it difficult to shift gait parameters in response to varying situations during walking . in a previous study , we reported a limited range of gait acceleration in patients with severe akinesia . since gait acceleration reflects the floor reaction force , the results suggested that pd patients tend to walk with a narrow range of change in floor reaction force . the present study expands this analysis by comparing simultaneously the range of step cycle with that of acceleration . for this purpose using our pgr , we recorded the daily walking profiles of 40 pd patients ( 26 men and 14 women ) with a mean age of 66.2  5.7 years ( sd ) ( table 1 ) . they represented all patients admitted to tokyo medical university hospital between june 2009 and march 2010 who could walk unaided and showed no peak - dose dyskinesia during the  on  time . they included 8 patient with hoehn and yahr stage i , 9 with stage ii , and 23 with stage iii . the clinical status was examined using the unified parkinson 's disease rating scale ( updrs ) motor score  on  state ( mean score of updrs part iii ; 19.2  7.1 ) . the study also included 17 age- and height - matched normal controls ( age , 64.7  4.5 years , 8 men and 9 women ) . matching for age and height was based on the finding that gait cycle and floor reaction forces are influenced by these two parameters . all procedures were conducted in accordance with the guidelines of the ethics committee of our institution . the portable pgr is a small device ( size , 8  6  2  cm , weight , 80  g ) ( figure 1(a ) ) that measures three dimensionally ( ax , ay , az ) the accelerations accompanied by ( 1 ) limb and trunk movements and ( 2 ) those induced by step - in and kick - off during gait [ 11 , 12 ] . the pgr is attached to the waist of the patient , and records the above signals at a sampling rate of 10  ms . when recording is completed , the absolute value of acceleration vectors ( a ; a = ax + ay + az ) is calculated and graphically displayed on the pc . the acceleration vectors caused by stepping can be distinguished from those by other limb and trunk movements or by unexpected artifacts , based on the mathematical method of   pattern matching ,  as reported previously [ 11 , 12 ] . the acceleration vectors related to stepping can be distinguished from those related to other movements , since the former has steep curves and appears rhythmically . furthermore , based on the mathematical method of   pattern matching ,  we identified the acceleration vectors caused by stepping from those by other trunk and limb movements . first , attention was focused on relatively strong signal region ( e.g. , a > 1  m / s ) in the acceleration time series , and a three - dimensional template wave ( ax , ay , az ) with a duration of about 0.5 sec was arbitrarily chosen around a local maximum point from that region ( figure 1(b ) ) . then , the cross - correlation cc(t ) was calculated between this wave and the whole time series at each time t using the following formula : ( 1)cc(t)(1/p)i=1p[ax(i)ax(i+t)+ay(i)ay(i+t)+az(i)az(i+t)]{(1/p)i=1p[ax(i)2+ay(i)2+az(i)2]}1/2{(1/p)i=1p[ax(i+t)2+ay(i+t)2+az(i+t)2]}1/2,where p is the length of the template wave . the obtained cross - correlation is a scalar time series , showing pronounced rhythmic peaks even when the initial signal is too noisy to visualize periodicity with naked eyes . if the acceleration change is caused by gait motion , the cc(t ) peaks exhibit alternate changes in magnitude with time due to left / right body sway during walking . thus , we can pick up correct peaks corresponding to one gait cycle ( figure 1(c ) ) . this  pattern matching method  also enabled us to distinguish the acceleration vectors caused by stepping from unexpected and large artifacts . moreover , although the pgr moved from the original position during the recording and the form of the acceleration vectors changed , we were able to identify the acceleration vectors continuously . changes in gait cadence ( steps per minute ) and gait accelerations cycle were examined during the 24  hrs . data were excluded when the step rate was < 20/hr . since gait acceleration correlates with the floor reaction forces , we measured gait acceleration as an index of floor reaction forces . the gait - related accelerations and duration of gait cycles were averaged for each 10  min recording ( see figures 24 ) . the graphs shown in these figures indicate that changes in the gait acceleration correlate with the gait cycle duration . the averaged logarithmic values showed normal distribution and the slope of the regression line was calculated using principal component analysis . figures 24 show the gait acceleration - cycle plot of each pd patient with the corresponding regression line ( thick line ) . the regression line , obtained from the summed average of 17 normal controls , is also shown ( thin line ) . in the gait acceleration - step cycle plot , which was obtained from one trial in each patient , thus , the ratio of the sd for each patient to the interindividual mean of sd for the normal controls was defined as  % alteration range .  in analysis of the percentage of alteration range , we used a cut - off level as 75% . for gait acceleration , the sd of the controls was 0.55 ( m / sec ) and , therefore , the 75% cut - off level was 0.41 ( m / sec ) . for the gait cycle , the sd of the controls was 0.11 ( sec ) with 0.082 ( sec ) representing the 75% cut - off level . the mean gait acceleration in pd was 2.00  0.38 ( m / sec ) ( n = 40 ) and was significantly lower than that of the normal controls ( 2.78  0.42 ( m / sec ) , n = 17 ) ( p < 0.001 ) . on the other hand , the mean gait cycle in pd patients was 1.14    0.10 ( sec ) ( n = 40 ) , which was not significantly different from that of the normal controls ( 1.12  0.11 ( sec ) , n = 17 ) . the percentage of alteration range was less than 75% in 12 of the 40 patients . figures 25 show the gait acceleration - cycle plots based on percentage of alteration range of the gait acceleration and cycle . the regression curves of patients with percentage of alteration range > 75% were similar to those of the controls ( n = 16 ) ( figure 2 ) . however , when percentage of alteration range of the acceleration or cycle decreased , the regression curve deviated showing the following three patterns . figure 3 shows the gait acceleration - cycle plots of patients with percentage of alteration range of the gait acceleration of < 75% . due to the narrow distribution of the gait acceleration values , the slope of the regression curve was steep ( steep type ) . figure 4 shows the gait acceleration - cycle plots of patients with percentage of alteration range of the gait cycle of < 75% . due to the narrow distribution of the gait cycle values , the slope of the regression curve was flat ( flat type ) figure 5 shows the gait acceleration - cycle plots of patients with percentage of alteration range of < 75% of both the gait acceleration and cycle . interestingly , the data of these patients showed a narrow scatter ( the lump type ) . the slope of the regression curve became reverse in one patient ( see figure 5(a ) ) . there were no significant differences in updrs iii between patients with the normal type ( 19.1  8.0 ) and those with steep , flat , and lump patterns ( 19.3  6.7 ) ( p = 0.93 ) . the present results using our newly developed long - term monitor device demonstrated narrowness in changes in gait accelerations and gait cycles . the gait acceleration - cycle plots , obtained from 24  hrs records of daily walking , showed deviation from the normal curves , and the extent of deviation was dependent on the decrease in the percentage of alteration range . the percentage of alteration range represented the ratio of the sd for each patient to the inter - individual mean of sd for the normal controls one possible reason for the narrow range of acceleration and cycle is the lack of movements within the house . with worsening of akinesia , physical activity tends to decrease in these patients , with subsequent decrease in walking even inside the house [ 11 , 12 ] . however , the patients of this study were asked to come to our hospital for wearing on and off the device during the recordings . thus , such travel to and from the hospital ensured some degree of walking . an alternative explanation for the narrow alteration range is related to a deficit in scaling of motor parameters , an elementary symptom in parkinsonism . flowers   postulated that pd patients can only execute movements of different amplitudes at a single and slow velocity and can not increase their movement velocity . consistent with this notion , the peak emg activity that can be generated in a muscle burst is limited in pd patients . the present finding might similarly suggest that pd patients could not vary the force and rhythm in their daily routine walking , and that pd patients consequently walk with a narrow range of floor reaction forces and step cycles . we reported previously the narrowness of floor reaction forces by calculating the slope of the regression line of the gait acceleration - cycle plot . in the present study , we confirmed this change using another method that measures the range of gait accelerations . although the mean values of the cycle was unchanged compared with controls , the range of the mean  1 sd range was narrow . the gait acceleration - step cycle plot showed that when a normal subject walks with a strong floor reaction force , the cycle increases simultaneously . the characteristic change in the shape of this curve is the narrowness of the range of force and/or rhythm . in patients who walked with a narrow range of change in gait acceleration ( i.e. , floor reaction forces ) , this type reflects short - step walking pattern , in which no effective floor reaction force is achieved despite the fast step . in contrast , patients who walked with a narrow range of change in the gait cycle , the regression curve was flat ( flat type ) . under this condition , the patient always walks with the preferred cycle , that is , monotone walking . other patients walked with a narrow range of both acceleration and cycle , with small scatter of gait acceleration and cycle data ( lump type ) . assessment of daily activities during 24  hrs should take into consideration the contamination with involuntary movements . resting tremor , occurring periodically at 5 - 6  hz , would be easily excluded using our analysis algorithm . however , dyskinesia is difficult to identify , since it occurs irregularly and in a variety of forms . severe disturbances of the gait parameters , associated with high activities , could reflect dyskinesia . taken together , our new device , which allows long - term monitoring with simultaneous recording of gait acceleration and cycle , identified deficits in the scaling of gait parameters , a potential pathomechanism underlying pd . analysis of daily physical activities , including walking , using this device might extend our understanding of motor symptoms of pd . to examine the range of gait acceleration and cycle in daily walking of patients with parkinson 's disease ( pd ) , we compared the gait of 40 patients with pd and 17 normal controls by using a newly developed long - term monitoring device that extracts gait - related accelerations from overall movements - related accelerations.the range of change in gait acceleration , relative to the control , was less than 75% in 12 patients . the present finding of narrow changes in gait parameters supports a notion that pd patients find it difficult to shift gait parameters in response to varying situations during walking .
to examine the range of gait acceleration and cycle in daily walking of patients with parkinson 's disease ( pd ) , we compared the gait of 40 patients with pd and 17 normal controls by using a newly developed long - term monitoring device that extracts gait - related accelerations from overall movements - related accelerations . 
 the range of change in gait acceleration , relative to the control , was less than 75% in 12 patients . 
 the range of change in gait cycle was less than 75% in 8 patients . 
 the range of changes in both parameters was less than 75% in 4 patients . 
 the results suggest narrow changes in gait parameters in pd .