## 3 Conclusions  
According to the number of articles published in each area, 40% of the papers are on gait analysis, 20% on instrumentation, 10% on computer modeling, 10% on the relationship between human and human-robot collaboration, 10% on general kinematics and kinetics, and 10% on finite element analysis. Basically, these proportions may have partially reflected various aspects of the current research field. However, these proportions do not reflect the tendency in future research. Potentially, there are still many areas for researchers to explore in the future. In the introduction of this Special Issue, there was a list of key words and topics indicating research directions, but there were not any articles in some of the proposed research topics. For example, there is no paper on artificial intelligence, machine learning, and deep learning; motion pattern recognition; bio-signal measurement and analysis; or bio-image measurement and analysis collected in this volume. One reason could be that this volume was open for a short period of time and thus researchers in these fields may not have recognized it while other special journals may have attracted potential authorsto publish their articles there. Another reason may be that the research activities in these proposed topics are less active than in other fields, such as those of the articles published in Volume 1. In terms of key words, there were no studies on electromyographs (EMGs) and electroencephalograms (EEGs) in this volume. In clinical practice, these electronic signals are so important as they are only a way to detect muscle and brain activities. For the patients with cerebral palsy and stroke, the analysis and interpretation of EMG and EEG may help us understand their movements deeply and then provide potential clues in clinical treatments and rehabilitation settings. It is time for researchers to pay more attention to projects on EMG and EEG. Another big and popular research direction is on artificial intelligence (AI). Compared to the progress in other subjects, AI application in biomechanics associated with clinical practice is relatively lacking. A major reason could be the difficulty in collecting data. In general, AI and machine learning require a significant amount of data as the base for modeling. It would be impossible to make reasonable AI models without the support of a large sample size. It is recognized that collecting data in a national or worldwide health system, i.e., hospitals and clinics, requires strict ethical permissions. On the other hand, researchers doing AI modeling may not clearly know which data they need and do not need at the beginning. Therefore, an ethics application may not be able to provide a detailed data collection plan. This may prevent researchers from obtaining useful data. Following the two aspects above, a key problem to be solved could be algorithms. Once you have both data and general AI methods, a suitable algorithm is needed to combine two things together. The design and testing of algorithms require not only a lot of time but also creative abilities. A successful algorithm could not only produce interesting results from the collected data but also link the research outcomes to clinical applications quickly. Usually, clinicians cannot make their own application directly from the data they collected. Therefore, it is necessary for researchers in biomedical engineering, mathematics, computing, biology, or similar fields to make a bridge between useful data and clinical applications. A good algorithm could bring in not only a good interpretation of clinical data but also a good application. Therefore, a creative algorithm could largely drive the research activities forward as shown in Figure 1.  
Figure 1: Summary of the conclusion for expecting new studies and results. As a whole, this editorial is missing many potential research directions while highlighting a few of them. It is certain that novel ideas and projects will be proposed by the clinicians and academic researchers who work together.