In this study, the relationship between VO2R and the acceleration-based movement indices, MA, MSD, and RMS—calculated from the measurement of trunk acceleration using a smart clothing system—was examined. Overall, the acceleration-based indices were significantly correlated with VO2R. The results of the regression analysis of each subject showed that MA, MSD, and RMS all fit the linear regressions, with MSD showing the best fit with the individual linear regressions. Using these acceleration indices, the different levels of exercise intensity defined in the Bruce protocol were clearly identified.
The overall correlation between the trunk acceleration with waist-worn accelerometer and the oxygen consumption has been shown previously [18, 19]. The results of this study showed that this correlation is also seen between the values measured by chest-worn accelerometer and the exercise intensity estimated from the oxygen consumption that is frequently used in the exercise prescription in the rehabilitation practice. In addition, we tested several indices of acceleration, such as MA and MSD—which are basic indices that represent the amplitude and fluctuation of values—and the RMS—which has been used in previous studies that quantified running using an accelerometer [18, 21]. Among these indices, MSD exhibited the strongest correlation with VO2R, and the least variability between the subjects. This may be related to the measurement of gravitational acceleration. Although the gravitational acceleration is constant, it is much larger than the dynamic component of acceleration, and a small measurement error rate may still influence the results of the measurement. Considering that the measurement of acceleration is affected by environmental conditions such as temperature  and that the necessity of frequent calibration would complicate measurement (which is the primary benefit of the accelerometer), measurement values that do not include gravitational acceleration could represent a better alternative. While MA and RMS are indices that include gravitational acceleration, MSD is an index of fluctuations from the moving average, which focuses more on the dynamic component of values. Although there may be more sophisticated methodology such as the use of autocalibration methodology to eliminate the gravitational acceleration , the simple solution to calculate moving standard deviation without complex data analysis can be easily applied regardless of the measurement devices. This is an advantage of the use of the MSD in the assessment of physical activity.
The correlation between the VO2R representing relative increase in oxygen consumption and the trunk acceleration is logically derived from the intensity of physical motion of the trunk. In fact, the trunk is the heaviest body segment [24, 25]; thus, its movement can largely affect oxygen consumption. Therefore, trunk movement can possibly provide more accurate measurements on exercise intensity than upper-limb movement, which varies extensively in patients with motor impairment. Although the measurement of trunk movement with a chest-mounted accelerometer may not be as easy as with wrist-worn accelerometers, the use of a smart clothing system can make it more feasible.
The acceleration indices also identified different levels of exercise tasks, which was defined by the speed and inclination of the treadmill in the Bruce protocol. This is reasonable considering that the large stride related to the high walking speed and inclination of treadmill requires a large vertical movement of the human body, and the cadence, which is the frequency of the steps, also increases to adjust to the high treadmill speed. Among the indices, MSD showed the least overlap in values between the levels 1, 2, 3, and 4, indicating better accuracy than the other indices in describing the physical intensity of the activity. However, the variability of the values at level 4 was markedly larger in the acceleration indices than the lower levels of the exercise task. This may be because of the variety of the participants' motions during the task; for example, the participants either walked or ran at this level and some of them used a handrail to control their body posture against the high speed and high inclination of the treadmill. Therefore, the acceleration index should reflect the participants' responses to the task in addition to the level of the exercise task itself, which may well reflect the exercise intensity in the acceleration indices.
Recent advancements in measurement technologies emphasize the potential feasibility of acceleration measurement in rehabilitation settings; however, several problems can occur when employing the commonly used measurement methodology and the indices of acceleration measurement for evaluating the activities of patients with movement disorders.
Two major types of accelerometers are commercially available: wrist-worn accelerometers and body-worn accelerometers. Wrist-worn accelerometers are easy to use, and numerous studies have shown the validity of activity measurements using these devices in healthy individuals [5, 26]. However, there is a concern regarding their usage in the case of patients with movement disorders. For patients with disabilities, the upper limb movements in daily life vary extensively for reasons such as upper-limb paresis or the use of walking aids; this may negate the validity of measurements provided by wrist-worn accelerometers.
As for body-worn accelerometers, the most commonly used devices are waist-worn pedometers. Although the step measurement with pedometers is widely used for activity quantification , the accuracy of measurements in patients with motor impairments has been questioned possibly owing to the low walking speeds, irregular movement patterns, or the use of walking aids. To utilize acceleration measurements in the field of rehabilitation, where most of the patients have motor impairment, it is necessary to develop measurement methods and indices that are resistant to variations in the motions of patients with motor impairment. On the contrary, there are several reports on chest-worn accelerometers for gait monitoring or posture monitoring [7–11]; however, the usability of chest-worn type devices for evaluating the intensity of the activity or the quantification of the activity has not been well investigated.
The present results would support the usability of the MSD of trunk movement with the chest-worn accelerometer in assessment of physical activity. Several studies have focused on heart rate-based activity monitoring using chest-strap or smart clothing monitors [27–31]. The MSD of trunk acceleration measures a similar activity; however, from a different perspective, while heart rate is a measure that reflects blood supply and is also correlated with oxygen supply, acceleration reflects actual physical movement as an output. In healthy individuals, the regression in each individual would be similar, as shown in this study. However, McGregor et al. reported that the relationship between acceleration measurements and oxygen consumption can vary with exercise experience . Accordingly, the relationship between supply and output may vary more in people with motor impairment. Evaluating this relationship will expand the possibilities of activity measurement. For example, quantification of the activity in terms of both supply and output could enable the assessment of exercise efficiency; a patient with severe paresis may need more blood supply, which results in an increase in the heart rate, while performing less physical movement than a patient with mild paresis (Supplementary data). Further exploration of these methodologies may provide a meaningful and clinically viable model for the use of activity monitoring in rehabilitation settings.
The small sample size and limited variety in sample are the limitations of this study. Because of the high correlation between the acceleration-based indices (RMS and device-specific parameters) and VO2 measurements reported previously [18, 19], the required minimum sample size is calculated as eight (1-β 0.95, α 0.05; calculated using the sample-size-calculating software G*Power, version 184.108.40.206) . This might be acceptable in an experiment with young, healthy subjects; however, further investigation for applying it in population with a wider range of variety is needed. As the final goal of this study is to investigate a methodology that is suitable for measuring the activity of the patients with movement disability, the validity of using the proposed index in clinical population should be tested with larger clinical samples.