# Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System
## 4 Discussion and Conclusions  
The validation of wearable motion capture systems is becoming a large and important issue due to the increasing availability of these technologies and solutions. In this study, a set of guidelines to design specific and dedicated validation protocols for wearable motion capture systems in comparison with GA gold standard optoelectronic systems is presented. It helps us to provide an approach of a framework for validation studies. To verify the applicability of the proposed guidelines, we have designed and carried out a case study with a single IMU-based system. The processing of raw data to extract gait parameters is based on a harmonic oscillator biomechanical model that we want to validate. Kinematic joints were not calculated, but only some spatiotemporal parameters. This study investigates the wearable IMU system's accuracy with respect to optoelectronic GA as the gold standard reference. The GA and the wearable measurements have been carried out simultaneously for a population of 10 subjects with four trials (40 datasets). Operators not involved in the experimental activity performed blind data analysis to assure the best impartiality. Better ergonomic fit can be achieved by using specially designed t-shirts and using a smaller IMU. The protocol we have designed according to the proposed guidelines has been demonstrated to be operatively simple and effective; the operators (one technician, two biomedical engineers, one physicist, and two clinicians) and the subjects involved in the experiments expressed good compliance and appreciation for its high usability. Data from the selected healthy cluster was collected without the interruption of programmed activities to evaluate clinical examinations over the pathological subjects present as usual in the GA laboratory. The time available allowed us to complete all the scheduled tests. Data were properly collected, except for two trials, whose reference GA data have been not successfully recorded by the optoelectronic system; these data were corrupted and have not supported the parameters computation except from the manual measurements taken by the skilled clinician. This could be described as a failure of the standard optoelectronic system (two cases out of 40 trials equal to a rate of 5%) with respect to the 100% success of the wearable system. The complexity of the optoelectronic systems has this intrinsic possibility. A more detailed discussion has to be done about the reference walking time value. The subject begins and ends walking outside the GA work volume. This means that a set of photocells could be used to measure the temporal duration of the task; alternatively, a skilled clinician with a stopwatch has to take these measurements. We do not have photocells and therefore in our case, this is a manual measurement taken by the skilled clinician. Thus, the percentage error of the total walking time is variable and operator-dependent. Its large values should be thought of in relation to the fact that the operator has an intrinsic reaction time in taking the measurement through a stopwatch. A typical reaction time of 0.5 s in the laboratory setup we have used (i.e., with 4-5 s duration of the trials) produces, respectively, for a total time of 4 s, the percentage accuracy error equal to 100 \(\times\) 0.5/4 = 12.5% and for a total time of 5 s, the percentage accuracy error equal to 100 \(\times\) 0.5/5 = 10%. It could be observed that the operator has to use the stopwatch twice (start and stop), so the estimated error could be even greater. In fact, according to the set up adopted and to the previous considerations, the value of the computed average % error is 13.9, in line with the expected outcomes. A point to be considered in such validation is that the motion capture system only uses very few steps and only one (the central stride) is considered as the regular representative cycle step. Therefore, when we use the wearable IMU system, we have to compare an average step resulting from a set of steps with a single stride.