It is well known that, to locate humans in GPS-denied environments, a lower limb kinematic solution based on Inertial Measurement Unit (IMU), force plate, and pressure insoles is essential. The force plate and pressure insole are used to detect foot-ground contacts. However, the use of multiple sensors is not desirable in most cases. This paper documents the development of an IMU-based FGCD (foot-ground contact detection) algorithm considering the variations of both walking terrain and speed. All IMU outputs showing significant changes on the moments of foot-ground contact phases are fully identified through experiments in five walking terrains. For the experiment on each walking terrain, variations of walking speeds are also examined to confirm the correlations between walking speed and the main parameters in the FGCD algorithm. As experimental results, FGCD algorithm successfully detecting four contact phases is developed, and validation of performance of the FGCD algorithm is also implemented. Practitioner Summary: In this research, it was demonstrated that the four contact phases of Heel strike (or Toe strike), Full contact, Heel off and Toe off can be independently detected regardless of the walking speed and walking terrain based on the detection criteria composed of the ranges and the rates of change of the main parameters measured from the Inertial Measurement Unit sensors.

Humanoid robots will become part of our everyday lives. They have biologically inspired features and psychologically complex properties. How will children interpret these ambiguous objects, discriminating between living and nonliving kinds? Do the biologically and psychologically inspired characteristics affect children’s understanding of the robots? How firm is the distinction that children make between living and nonliving objects? To address these questions, 120 children ranging three to five years initially viewed video clips that depicted humanoid robots interacting with a human experimenter on two different dimensions (mobility and, psychologically contingent behavior). The subjects then answered simple questions that probed their animacy judgments and property projections about the robot. The results showed that children’s animacy assessments about humanoid robots differed by age. When judging the robot’s life status, its mobility was important for four-year-olds and, the psychological contingency for five-year-olds. In terms of the robot’s reasoning abilities, the majority of four-year-old children clearly understood biological properties, regardless of the robots’ features. However, when reasoning about psychological properties, even five-year-olds occasionally relied on robots’ features such as their contingent behaviors. Moreover, the children attributed some but not all animate properties to the robots. Although rent findings show that children possess naïve theories, they do not seem to have a consistent and logical theory of “aliveness,” and they apparently develop the concept of a robot by acquiring knowledge about how this boundary object differs from living entities.

The central perspective of this review is to categorize research in Human Motion Recognition (HMR) over the past decade into two significant categories: vision sensor-based (VS) methods and wearable sensor-based (WS) methods. Within each category, research is further assessed from the viewpoints of sensors, classification algorithms, datasets, gesture types, target body parts, and performance. This approach allows for a comprehensive assessment of the overall research trends and technological advancements in HMR. Both VS methods and WS methods present their own sets of advantages and challenges. VS methods face challenges related to limited workspace, varying lighting conditions, occlusion, and complex image processing. Conversely, WS methods, compared to VS methods, deals with challenges associated with multiple sensor calibration, intrusiveness, and magnetic field mapping due to sensor placement. As such, the choice between these methods depends on the specific application, the required level of accuracy, and user preferences. Gaining insights into the nature of various HMR methods and staying informed about recent research trends is of utmost importance. By the end of this review, readers will gain a comprehensive and systematic understanding of the latest developments in HMR techniques, which will serve as a valuable resource for researchers and practitioners alike.