## 4 Discussion
However, conventional algorithms are usually mathematically simple and computationally inexpensive, and do not require large amount of training data. Nonetheless, in this study, employing a mathematically simple classification algorithm such as LDA was shown to be at the expense of being limited to using a training paradigms with lower variability (e.g., subject-dependent) and/or instrumenting multiple body locations. Continuous task classification implemented in this study has the capability to classify data as they are being captured, which is crucial in the context of developing task monitoring scenarios and assistive technologies. In individuals with robotic orthosis/exoskeleton, it would increase the intuitiveness/volitical behavior of the device and enables smooth transitions between locomotor activities. Continuous classification would also allow adaptive assistance [45], predicting fall risks and intervening on these risks to mitigate falls [46]. The reduced levels of flexibility to adapt to new tasks and difficulty in performing transitions in individuals with PD [29; 30] further highlights the advantages of developing such frameworks for tracking characteristics of transitional periods as well as to track steady-state progress. The study has some limitations. We considered the toe-off event during the transition period as the initiation of the upcoming task. Toe-off could be a relatively accurate approximation of the task initiation where it occurs close to the physical transition point. However, toe-offs occurred at greater distances may negatively impact the outcomes, since a large portion of the gait cycle labeled as the upcoming locomotor task is still within the previous mode. This could result in high misclassification rates especially during the transition period. In future studies, the problem could be addressed by either modifying task separation events, or separating data into steady-state and transitional periods, and performing separate evaluation for each state [47]. Another limitation of this study is that we used motion capture data and not the data from the actual wearable IMU sensors. Real-world issues associated with the physical wearable sensors such as difficulties with aligning the sensors [48], and inherent bias and drift errors in actual IMUs [49] may affect system performance, and thus should be taken into consideration in future studies. While PD patients did not experience freezing of gait during the experiments, their mechanics (joint kinematics) were observed to be different from control subjects [50]. We also observed some \"festination\" and unilateral loss of arm swing (a form of automatic motion) which is called bradykinesia. While these people did have observable differences, the purpose of this study was not to quantify it. However, disease associated symptoms such as tremor and bradykinesia could be more severe in patients with higher stages of the PD, which may deteriorate the performance of classification algorithms especially in subject independent scenarios. Further, a small sample of subjects participated in this study. Future research should investigate the effects of using data from varied levels of disease severity and a larger subject pool to accommodate for the potential across-subject variabilities. For LDA, we selected features based on previous research for intent recognition frameworks. The selected features are computationally inexpensive and have functioned relatively well for intent recognition [23; 24]. Neural networks (e.g., LSTM) learn features themselves alleviating the need for manual feature engineer. We attempted to demonstrate this as an advantage of such models in minimizing engineering bias. Feature engineering approaches are problem specific and thus one set of features might not generalize well across all the target activities. Thus, optimal features could significantly vary from one task to another which negatively impacts the transition of such approaches into clinical setting by making them task dependent.