The basic understanding of human movement and control of human movement stems largely from laboratory measurements where human movement can be quantified with high precision and accuracy, but where the artificial environment compromises ecological validity. A good example for this issue was demonstrated in a recent investigation; specifically that the walking gait pattern of healthy individuals in a laboratory changed as a function of how many researchers were present during the experiment. Observations like these underscore that study volunteers adapt their behavior to the specific laboratory environment and warrant the question of how well we can transfer our lab-based understanding of gait patterns and the underlying neuromuscular control system to walking during daily living. Another research area where lab-based movement assessments have led to conflicting findings is the field of sports injury prevention: Many neuromuscular training programs have been shown to be effective in reducing the sport injury rate in athletes by 30-50% or more in a variety of different multi-directional sports. Nevertheless, lab-based assessments of the same athletes who completed those training programs were often not able to detect improvements in motor control of sport-specific movements or a reduction in joint loading, two factors thought to be closely linked with sport injury risk. This disconnect suggests that lab-based assessments of movement and motor control are often poor indicators of player behavior during real-game scenarios and may limit our ability to screen athletes for injury risk or monitor their progress in rehabilitation. These examples highlight that we should strive for the assessment and investigation of human movement and motor control in natural environments, i.e. where individuals, patients, athletes, or other groups of interest perform, explore, and interact under real-world conditions.
Recent developments of body-worn sensors and of advanced data analysis algorithms increasingly allow us to study even the most complex features of human movement and human motor control in the natural environment. Early systems counted steps, measured heart rates and/or determined positions, e.g. of players on a sports field, or of an athlete exercising in the environment. More advanced systems determine not only a single reference position, but are capable of determining the position and orientation of all main body segments. These systems can be combined with mobile force or pressure measurements and/or with muscle activity measurements to estimate joint and muscle forces that produce whole-body movements. In parallel, developments in musculoskeletal simulation and machine learning continues to further our understanding of the sensorimotor control principles that guide and control whole-body movements.
Other novel approaches allow testing of hypotheses about human movements through marker-less motion analysis, i.e. deep learning algorithms that can track posture and movement of one or multiple persons from standard video footage and minimal user input. Further advances in sensor battery life, data storage solutions and handling of big data enable to study human movement over longer time frames (hours, days, weeks), which can provide more meaningful insight into their behaviour over time, e.g. motor improvements during training or rehabilitation.
With all of these recent technological developments, analyses of how humans move and how they coordinate their body segment movements in real-life situations and in actual interactions with the environment become possible providing the opportunity to make a big leap in our understanding of healthy human behaviour, pathological movement, and rehabilitation and prevention of movement-related injury and disease.
In the current research topic, we invite contributions that aim at improving the ecologic validity of human movement and motor control research. We invite manuscript with topics including but not limited to:
1) The intricate coordination of segment movements that allow us to navigate complex and challenging environments, e.g. moving and balancing on unstable or slippery surfaces
2) Adaptation of human motor control with ageing, disease, injury, athletic training, and/or rehabilitation in natural environments
3) Motor and perceptual exploration of humans in natural environments
4) Human movement and motor control during interaction with objects or with other humans in natural environments
5) The comparison of laboratory-based assessments of human movement and motor control with assessments under real-world conditions
6) Technological advancements for the assessment of human movement and motor control in natural environments, e.g. novel analytical approaches to study whole-body motor control or novel sensor/software technologies to quantify human movement.
We particularly welcome studies that analyse whole-body human motion and the underlying sensorimotor control system, i.e. studies that employ technologies to record and extract information from multiple-sensor systems. We accept contributions using the following article types: ‘Original research’, ‘brief research report’, ‘systematic review’, ‘narrative review’, ‘mini-review’, ‘data report’, and ‘technology and code’.
The basic understanding of human movement and control of human movement stems largely from laboratory measurements where human movement can be quantified with high precision and accuracy, but where the artificial environment compromises ecological validity. A good example for this issue was demonstrated in a recent investigation; specifically that the walking gait pattern of healthy individuals in a laboratory changed as a function of how many researchers were present during the experiment. Observations like these underscore that study volunteers adapt their behavior to the specific laboratory environment and warrant the question of how well we can transfer our lab-based understanding of gait patterns and the underlying neuromuscular control system to walking during daily living. Another research area where lab-based movement assessments have led to conflicting findings is the field of sports injury prevention: Many neuromuscular training programs have been shown to be effective in reducing the sport injury rate in athletes by 30-50% or more in a variety of different multi-directional sports. Nevertheless, lab-based assessments of the same athletes who completed those training programs were often not able to detect improvements in motor control of sport-specific movements or a reduction in joint loading, two factors thought to be closely linked with sport injury risk. This disconnect suggests that lab-based assessments of movement and motor control are often poor indicators of player behavior during real-game scenarios and may limit our ability to screen athletes for injury risk or monitor their progress in rehabilitation. These examples highlight that we should strive for the assessment and investigation of human movement and motor control in natural environments, i.e. where individuals, patients, athletes, or other groups of interest perform, explore, and interact under real-world conditions.
Recent developments of body-worn sensors and of advanced data analysis algorithms increasingly allow us to study even the most complex features of human movement and human motor control in the natural environment. Early systems counted steps, measured heart rates and/or determined positions, e.g. of players on a sports field, or of an athlete exercising in the environment. More advanced systems determine not only a single reference position, but are capable of determining the position and orientation of all main body segments. These systems can be combined with mobile force or pressure measurements and/or with muscle activity measurements to estimate joint and muscle forces that produce whole-body movements. In parallel, developments in musculoskeletal simulation and machine learning continues to further our understanding of the sensorimotor control principles that guide and control whole-body movements.
Other novel approaches allow testing of hypotheses about human movements through marker-less motion analysis, i.e. deep learning algorithms that can track posture and movement of one or multiple persons from standard video footage and minimal user input. Further advances in sensor battery life, data storage solutions and handling of big data enable to study human movement over longer time frames (hours, days, weeks), which can provide more meaningful insight into their behaviour over time, e.g. motor improvements during training or rehabilitation.
With all of these recent technological developments, analyses of how humans move and how they coordinate their body segment movements in real-life situations and in actual interactions with the environment become possible providing the opportunity to make a big leap in our understanding of healthy human behaviour, pathological movement, and rehabilitation and prevention of movement-related injury and disease.
In the current research topic, we invite contributions that aim at improving the ecologic validity of human movement and motor control research. We invite manuscript with topics including but not limited to:
1) The intricate coordination of segment movements that allow us to navigate complex and challenging environments, e.g. moving and balancing on unstable or slippery surfaces
2) Adaptation of human motor control with ageing, disease, injury, athletic training, and/or rehabilitation in natural environments
3) Motor and perceptual exploration of humans in natural environments
4) Human movement and motor control during interaction with objects or with other humans in natural environments
5) The comparison of laboratory-based assessments of human movement and motor control with assessments under real-world conditions
6) Technological advancements for the assessment of human movement and motor control in natural environments, e.g. novel analytical approaches to study whole-body motor control or novel sensor/software technologies to quantify human movement.
We particularly welcome studies that analyse whole-body human motion and the underlying sensorimotor control system, i.e. studies that employ technologies to record and extract information from multiple-sensor systems. We accept contributions using the following article types: ‘Original research’, ‘brief research report’, ‘systematic review’, ‘narrative review’, ‘mini-review’, ‘data report’, and ‘technology and code’.
