**About the Editors**

**Francesco Di Nardo** is Senior Staff Scientist in the Movement Analysis Lab, Department of Information Engineering Universita Politecnica delle Marche, Ancona, Italy. Within the same ` Department, he is Head of the section "Acquisition Systems and Data Processing". He served as Professor of Medical Informatics in the Bachelor Degree in Biomedical Engineering, Universita` Politecnica delle Marche, for the academic year 2019/2020. He is currently Associate Editor of the journal *IRBM* (*Innovation and Research in BioMedical Engineering*, Elsevier) and Topic Editor for the journal Electronics (MDPI). In addition, he is the contact person responsible for the region of Marche for the Italian Society of Movement Analysis in Clinics (SIAMOC), Senior Fellow of Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (BOHNES), and a founding member of the association Italian National Group of Bioengineering (GNB). The Universita` Politecnica delle Marche, where he is currently associated, is also where he completed his master's degree in Electronic Engineering in 2000 and received his Doctor of Philosophy (PhD) in Artificial Intelligence Systems in 2005. His main research activities include the areas of biomedical signal processing (filtering, feature extraction, pattern recognition, time–frequency analysis, application of neural networks to biosignals, statistical gait analysis) and interpretation (physiology, clinics, sport), particularly the acquisition and processing of surface electromyography (EMG) signals to assess muscular function during gait tasks. In this and other biomedical fields, he is author and co-author of more than 130 publications, including full papers in refereed international journals, chapters in international books, and conference papers.

**Sandro Fioretti** received his Dr Eng degree in Electronic Engineering from the University of Ancona, Italy, in 1979, and diploma for the specialist course in "Engineering of Control Systems and Automatic Computing" from Rome University "La Sapienza" in 1984. Since 2000, he has served as Associate Professor of Bioengineering at Universita Politecnica delle Marche, ` Ancona, Italy. He has participated in various European and National research projects in the field of Rehabilitation Engineering. His main research interests are in movement analysis, stereophotogrammetry for movement analysis (with markers and markerless), linear and nonlinear filtering of biomechanical signals, joint kinematics, modeling and identification of postural control, static and perturbed posturography, telematic applications for movement analysis, rehabilitation engineering, and analysis of biomedical signals.

## **Preface to "Recent Advances in Motion Analysis"**

In recent years, research in the field of motion analysis has contributed to the increasing analysis of motor tasks and activities performed in ambulatory or domestic/work environments. This requires the use of instrumentation that largely differs from that used in classic structured environments such as (clinical) movement analysis laboratories. In particular, the advent of miniaturized sensors, such as inertial sensors with low-cost, wearable, and wireless characteristics, has captured the attention of researchers as evident in the scientific literature of the last decade. Attention has mainly been focused on problems related on how to ameliorate the overall level of accuracy, the proper placement of sensors with regard to which body segment(s), and feature extraction from standardized motor tasks, e.g., f.i., gait, or sit-to-stand or squat. The possibility to monitor generic activities of daily life is nowadays possible as far as the recording of data is concerned. Toward this purpose, we are assisting through the fruitful integration of movement analysis with tools and methods derived from Internet of Things (IoT), automotive, robotics, and gaming contexts along with the availability of low-power consumption and high memory capacity of modern electronic devices. Besides problems of acceptability and usability of the systems, that imply use of the minimum possible number of sensors as well as topics concerning subject privacy, it is the interpretation of the acquired signals that remains a big challenge. The problem of automatic recognition of generic daily activities in long-term monitored signals is an open problem. Valuable contributions are expected from computational intelligence methods such as f.i., artificial neural networks, fuzzy logic rules, and automatic classification methods. In this context, this Special Issue on recent advancements in movement analysis attempts to cover some of the methodological and applicative aspects of modern movement analysis.

> **Francesco Di Nardo, Sandro Fioretti** *Editors*

*Review*
