Human Movement Behavior Research Articles

A reach motion generation algorithm based on posture memories.

Most existing models/algorithms for simulating goal-directed human motions were designed to generate a single "realistic" motion for a given input scenario. This study presents a novel reach motion generation algorithm utilizing multiple posture memories. The algorithm aims to compute and visualize a set of human reach motions that approximates the full range of physically and physiologically feasible human motions for a given input scenario. The algorithm utilizes posture memories constructed specifically for an individual worker using a probabilistic posture generation and registration process. The posture memories relate a hand position to the set of postures that place the individual's hand in its vicinity. When given an input scenario, the algorithm first generates different hand paths connecting the starting and ending hand positions specified in the scenario. Then, for each hand path, the algorithm produces different "feasible" motions by selecting and connecting multiple postures stored in the posture memories; the postures corresponding to the hand positions along the hand path are utilized. The proposed algorithm helps understand the impacts of workplace design on the range of feasible human motion behaviors, and, thereby, contributes to the computer-aided ergonomics design of work tasks and workplaces.

Visual Perception for Multiple Human–Robot Interaction From Motion Behavior

Visual perception is an important component for human–robot interaction processes in robotic systems. Interaction between humans and robots depends on the reliability of the robotic vision systems. The variation of camera sensors and the capability of these sensors to detect many types of sensory inputs improve the visual perception. The analysis of activities, motions, skills, and behaviors of humans and robots have been addressed by utilizing the heat signatures of the human body. The human motion behavior is analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target in the omnidirectional (O-D) thermal images. The process of human target identification and gesture recognition by traditional sensors have problem for multitarget scenarios since these sensors may not keep all targets in their narrow field of view (FOV) at the same time. O-D thermal view increases the robots’ line-of-sights and ability to obtain better perception in the absence of light. The human target is informed of its position, surrounding objects and any other human targets in its proximity so that humans with limited vision or vision disability can be assisted to improve their ability in their environment. The proposed method helps to identify the human targets in a wide FOV and light independent conditions to assist the human target and improve the human–robot and robot–robot interactions. The experimental results show that the identification of the human targets is achieved with a high accuracy.

Highly sensitive flexible capacitive pressure sensor with a broad linear response range and finite element analysis of micro-array electrode

With the continuous development of wearable electronics, health care and smart terminals, highly performance flexible pressure sensors present a huge application prospect. In this study, by introducing the micro-array structured electrodes and dielectric layers with high dielectric constant, capacitive pressure sensor fabricated with a brand new preparation strategy and highly sensitive is proposed. The prepared micro-array structure is the basis for sensors with high sensitivity. Besides, the contact area between the two electrodes changes from linear to planar with the increased loading, which result in a wider linear responding range. In addition, by introducing ceramic dielectric material-barium titanate (BT) fillers into the dielectric layer to increase it’s the dielectric constant, the sensitivity of the sensor shows two-fold increase. Moreover, the sensitivity gradients can be tuned by changing the loading contents of BT particles. Hence, compared with parallel board capacitive sensors with ordinary dielectric layer, these sensors exhibit excellent performance as follow, high sensitivity (up to 4.9 kPa−1) under low pressure range (0–2500 Pa), low detection limit (<1.7 Pa), short response time (<50 ms), a stable response over 5000 loading-unloading cycles, bending stability and an adjustable sensitivity. Further, the flexible pressure sensor can detect the pressure of the water droplets and monitor human movement behavior. With the facile design and excellent comprehensive properties, the flexible pressure sensor provides a new approach to improve the sensitivity and shows a broad application prospects in the wearable electronics, health care and smart terminals.

Eco-friendly and biodegradable cellulose hydrogels produced from low cost okara: towards non-toxic flexible electronics

With increasing resource shortage and environmental pollution, it is preferable to utilize materials which are sustainable and biodegradable. Side-streams products generated from the food processing industry is one potential avenue that can be used in a wide range of applications. In this study, the food by-product okara was effectively reused for the extraction of cellulose. Then, the okara cellulose was further employed to fabricate cellulose hydrogels with favorable mechanical properties, biodegrablability, and non-cytotoxicity. The results showed that it could be biodegraded in soil within 28 days, and showed no cytotoxicity on NIH3T3 cells. As a proof of concept, a demostration of wearable and biocompatible strain sensor was achieved, which allowed a good and stable detection of human body movement behaviors. The okara-based hydrogels could provide an alternative platform for further physical and/or chemical modification towards tissue engineering, medical supplies, or smart biomimetic soft materials.

INVESTIGATING THE RHYTHMS OF HUMAN MOVEMENTS IN GENEVA LAKE REGION USING MDC DATA

Abstract. Movement data are becoming extensive and comprehensive with the advent of GPS (global positioning system) and pervasive use of smartphones, which has led to an increasing rate of studies about movement such as mobility pattern of oil spills, taxies, storms and animals. Studying the movement of people has long been the topic of much thought and debate among researchers within the field of transportation, social issues, and policy. One of the basic prerequisites for studying human movement behavior is modeling the movement, which show how people move so that the effect of different variables can be revealed. For this purpose, this research intends to deploy the concept of activity space (i.e., the part of the space in which a person is active) and its determinants to display the trajectory of individuals, and then modeling the effect of different variables on human mobility behavior. This study explores the effect of time (movement on weekends and weekdays) and demographic (age, gender, occupation state) factors on the characteristics of human mobility pattern and analyzes the extent to which the mobility pattern of different group of people is related to time by using Swiss human movement sample dataset, called MDC. These movement characteristics can be used later in a wide range of applications, such as predictions, urban planning, and traffic forecasting.

Bracelet-based Monitoring and Analysis Tool for Daily Life Behaviors

As China’s economy develops and people’s attitudes change, the birth rate of newborns in China is decreasing year by year, but the population over 65 is increasing year by year. China is moving towards an aging society. The problem of providing for the aged is gradually becoming a national problem. Faced with such a social problem, we want to solve such a problem through the development of science and technology. In response to the fall of an elderly person and the fact that he was left unattended, This paper presents a monitoring and analysis tool of daily life behavior based on bracelet, The tool collects data about the human body through a bracelet sensor, The machine learning algorithm is used to recognize the human motion behavior, and then the neighboring people or relatives are notified by alarms or text messages, so as to achieve the effect of guardianship, which not only avoids the elderly falling unnoticed, but also reduces the difficulty of guardianship without watching all the time.

A Dual-Accelerometer System for Detecting Human Movement in a Free-living Environment

Accurate measurement of various human movement behaviors is essential in developing 24-h movement profiles. A dual-accelerometer system recently showed promising results for accurately classifying a broad range of behaviors in a controlled laboratory environment. As a progressive step, the aim of this study is to validate the same dual-accelerometer system in semi free-living conditions in children and adults. The efficacy of several placement sites (e.g., wrist, thigh, back) was evaluated for comparison. Thirty participants (15 children) wore three Axivity AX3 accelerometers alongside an automated clip camera (clipped to the lapel) that recorded video of their free-living environment (ground truth criterion measure of physical activity). Participants were encouraged to complete a range of daily-living activities within a 2-h timeframe. A random forest machine-learning classifier was trained using features generated from the raw accelerometer data. Three different placement combinations were examined (thigh-back, thigh-wrist, back-wrist), and their performance was evaluated using leave-one-out cross-validation for the child and adult samples separately. Machine learning models developed using the thigh-back accelerometer combination performed the best in distinguishing seven distinct activity classes with an overall accuracy of 95.6% in the adult sample, and eight activity classes with an overall accuracy of 92.0% in the child sample. There was a drop in accuracy (at least 11.0%) when other placement combinations were evaluated. This validation study demonstrated that a dual-accelerometer system previously validated in a laboratory setting also performs well in semi free-living conditions. Although these results are promising and progressive, further work is needed to expand the scope of this measurement system to detect other components of behavior (e.g., activity intensity and sleep) that are related to health.

A Lightweight Ultrasound Probe for Wearable Human–Machine Interfaces

A novel sensor technology is urgently required to sense the human motion behavior for wearable human–machine interfaces (HMIs) in a wide spectrum of applications, such as rehabilitation. The inherent limitations of surface electromyography signals and the shortages of the existing ultrasound-based sensors constrain the development of wearable devices from actually being applied to improve our life quality. In this paper, a lightweight A-mode probe, consisting of housing and polyvinylidene fluoride (PVDF) membrane, is proposed to enhance the detection of morphological changes of deep-layered muscles in the wearable HMI context. The PVDF-based probe, with 0.75-g weight and 0.6-mm thickness, is designed, simulated, fabricated, and validated in hand gesture recognition. Its classification accuracy achieves 97.64%±1.83%, which is practically accepted for the usage requirements of the existing wearable HMIs. This paper paves the way for feasible wearable HMIs in real-world applications with lightweight ultrasound probes.

Multi-level medical periodic patterns from human movement behaviors.

Human movement behaviors could reveal many interesting medical patterns. Due to the advances in location-aware devices, a large volume of human movement behaviors has been captured in the form of spatio-temporal trajectories. These spatio-temporal trajectories are useful resources for medical data mining, and they could be used to classify which trajectory passes through medical centres and which one does not. Traditional approaches utilise time-series datasets while ignoring spatio-temporal semantics in order to detect periodic patterns in medical domains. They also fail to consider the inherent hierarchical nature of patterns. We investigate a medical data mining framework that generates multi-level medical periodic patterns. A Geolife dataset is used to test the feasibility and applicability of our framework. Experiments demonstrate that the proposed framework successfully distinguishes those who periodically visit medical centres from those who do not, and also to find multi-level medical periodic patterns revealing interesting hierarchical medical behaviours. One potential application includes an automated personalised medical service. For instance, medical institutions can send personalised relative medicine information to people who regularly visit certain medical centres. It will be useful for the discovery and diagnosis of diseases for patients.

Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar

A real-time behavior detection system using millimeter wave radar is presented in this article. Radar is used to sense the micro-Doppler information of targets. A convolution neural network (CNN) is further implemented in the detection and classification of the human motion behaviors using this information. Both the convolution layers and architecture of CNNs are presented. The analysis on loss and accuracy of training results is also shown. The experimental result indicates a precise determination of human motion behavior detection using the proposed system.

Inverse discounted-based LQR algorithm for learning human movement behaviors

Recently, there has been an increasing interest towards understanding human movement behaviors. In this regard, one of the approaches is to retrieve the unknown underlying objective function that the human has to optimize while achieving a certain movement behavior. Existing research of behavioral understanding merely depends on predefined optimality criteria, where the minimum time, minimum variance or/and minimum effort are mainly adopted. These criteria are assumed to be constant, where the human is assumed to have the same preferences during the movement duration. However, in this paper, the optimality criteria underlying the kinematic characteristics of a certain human behavior are assumed to be exponentially discounted to account for the change in the human preferences that could happen while achieving this behavior. A new Inverse Discounted-based Linear Quadratic Regulator (ID-LQR) algorithm is developed in the light of Inverse Optimal Control (IOC) framework to find out the discounted cost function that could reproduce the measured human behavior perfectly. Meanwhile, an Incremental version of the ID-LQR algorithm is proposed to continuously refine the so far learned cost function in the case of sequentially presented demonstrations. The saccadic eye gaze movement is studied as an example to quantify both the proposed ID-LQR and Inverse ID-LQR approaches. Simulation results are encouraging and show that the saccadic trajectories generated by ID-LQR approach match the experimental data in many aspects, including position and velocity profiles of saccades. Moreover, when it is assessed by a subsequent set of scenarios, the incremental ID-LQR algorithm confirms its capability to generalize the so far retrieved cost function for the unseen saccadic demonstrations.

Motion Analysis-Based Application for Enhancing Physical Education

Physical education plays an important role in positively influencing students to develop a good learning process and a healthy lifestyle. Moreover, physical education assists students and athletes in developing muscular strength, maintaining fitness, and improving cardiovascular health. Issues arise when the lack of actual pattern of movement of children in sports leads to difficulties in implementing the correct movement, thereby decreasing the interest of students in physical education and students may suffer from muscular injury if they make the wrong movement. In this study, a human motion analysis method is utilized to overcome the abovementioned issues. Human motion analysis supports physical education in analyzing human movement behavior or patterns in conducting physical activities. Thus, this paper aims to develop an application for teaching students to perform the correct movement during physical education courses using motion analysis, which can help reduce the threats to physical education and solve its main issue, which is injury in athletes. The proposed application is expected to increase the interest and participation levels of students in physical education courses.

Learning Hand Movement Interaction Control Using RNNs: From HHI to HRI

A key problem in robotics is enabling an autonomous agent to perform human-like arm movements in close proximity to another human. However, modeling the human decision and control process of the movement during dyadic interaction presents a challenge. Although, most prior approaches rely on multicomponent robot motion planning architectures, we use data of two humans performing interfering arm reaching movements to extract and transfer interaction behavior control skill to a robotic agent. A recurrent neural network-based framework is constructed to learn a policy that computes control signals for a robot end effector in order to replace one human. The learned policy is benchmarked against unseen interaction data and a state-of-the-art learning from demonstration framework in simulated scenarios. We compare several architectures and investigate a new activation function of three stacked tanh(). The results show that the proposed framework successfully learns a policy to imitate human movement behavior control during dyadic interaction. The policy is transferred to a real robot and its feasibility for close-proximity human-robot interaction is shown.

Integrated Use of Agent Based Modelling (ABM) with Wearable Sensors for Personal Exposure Assessment

Considering the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for the urban area of Thessaloniki, Greece. The model feeds into population-based exposure assessment, basing its estimations onto emerging behaviours of all the heterogeneous entities (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents respectively. Time-use survey outputs and personal sensor collected data were associated with human agent rules, aiming to model representative to real-world routines. In addition, time-geography of exposure data, derived from a personal sensors campaign on 100 households, was used to inform the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM2.5 concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 32% whereas exposure between two neighbours can vary by as much as 77%, due to the prevalence of different behaviours. This approach permits the cost-effective construction of time-activity diaries and daily exposure profiles, considering different microenvironments and socioeconomic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can serve as a tool to evaluate probable impacts of public health policies prior to implementation, reducing the time and cost required to identify effective measures.

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm’s estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human’s behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

Human-Like Motion Planning Based on Game Theoretic Decision Making

Robot motion planners are increasingly being equipped with an intriguing property: human likeness. This property can enhance human–robot interactions and is essential for a convincing computer animation of humans. This paper presents a (multi-agent) motion planner for dynamic environments that generates human-like motion. The presented motion planner stands out against other motion planners by explicitly modeling human-like decision making and taking interdependencies between individuals into account, which is achieved by applying game theory. Non-cooperative games and the concept of a Nash equilibrium are used to formulate the decision process that describes human motion behavior while walking in a populated environment. We evaluate whether our approach generates human-like motions through two experiments: a video study showing simulated, moving pedestrians, wherein the participants are passive observers, and a collision avoidance study, wherein the participants interact within virtual reality with an agent that is controlled by different motion planners. The experiments are designed as variations of the Turing test, which determines whether participants can differentiate between human motions and artificially generated motions. The results of both studies coincide and show that the participants could not distinguish between human motion behavior and our artificial behavior based on game theory. In contrast, the participants could distinguish human motions from motions based on established planners, such as the reciprocal velocity obstacles or social forces.

A Hybrid Framework for Understanding and Predicting Human Reaching Motions.

Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions. For understanding, a model-based approach is required as the human motor control system relies on the biomechanical properties to control and execute actions. The model-based control models explain human motions descriptively, which in turn enables predicting and analyzing human movement behaviors. In motor control, reaching motions are framed as an optimization problem. However, different optimality criteria predict disparate motion behavior. Therefore, the inverse problem—finding the optimality criterion from a given arm motion trajectory—is not unique. This paper implements an inverse optimal control (IOC) approach to determine the combination of cost functions that governs a motion execution. The results indicate that reaching motions depend on a trade-off between kinematics and dynamics related cost functions. However, the computational efficiency is not sufficient for online prediction to be utilized for HRI. In order to predict human reaching motions with high efficiency and accuracy, we combine the IOC approach with a probabilistic movement primitives formulation. This hybrid model allows an online-capable prediction while taking into account motor variability and the interpersonal differences. The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.

Liquid Metal Enabled Flexible Electronic System for Eye Movement Tracking

Eye movement tracking is concerned with many important areas of research, of which tracking with a traditional scleral search coil system is deemed the gold standard. However, since such coil system requires anaesthetic use due to the stiffness of the copper wire coil in the system, it is of great importance to improve the wearing sensation experience. Here, an innovative electronic system for eye movement recording is proposed and realized, in which a liquid metal coil is introduced as the core element, attempting to provide a comfortable eye sensation. Theoretical investigation shows that this type of coil has performance similar to the traditional copper coil, which implies it is suitable for eye movement tracking. It has been demonstrated that such liquid metal coil is soft, flexible, and easy to produce, indicating that the coil is potentially favorable as a wearable medical device. Experiments show that the variance fitting curve between the induced voltage and rotation angle measured with the liquid metal coil during eye movement accords well with that of the classical copper scleral search coil, with both determination coefficients over 0.98, which confirms the reliability of the new coil. Overall, the feasibility of the liquid metal coil system suggests a useful approach for detecting human eye movement behavior and related diseases, which may facilitate disease diagnosis and prediction.

Piezoresistive Pressure Sensor Based on Synergistical Innerconnect Polyvinyl Alcohol Nanowires/Wrinkled Graphene Film.

Piezoresistive sensor is a promising pressure sensor due to its attractive advantages including uncomplicated signal collection, simple manufacture, economical and practical characteristics. Here, a flexible and highly sensitive pressure sensor based on wrinkled graphene film (WGF)/innerconnected polyvinyl alcohol (PVA) nanowires/interdigital electrodes is fabricated. Due to the synergistic effect between WGF and innerconnected PVA nanowires, the as-prepared pressure sensor realizes a high sensitivity of 28.34 kPa-1 . In addition, the device is able to discern lightweight rice about 22.4 mg (≈2.24 Pa) and shows excellent durability and reliability after 6000 repeated loading and unloading cycles. What is more, the device can detect subtle pulse beat and monitor various human movement behaviors in real-time.

High‐Resolution Patterning and Transferring of Graphene‐Based Nanomaterials onto Tape toward Roll‐to‐Roll Production of Tape‐Based Wearable Sensors

AbstractThis paper reports on a simple and versatile method for patterning and transferring graphene‐based nanomaterials onto various types of tape to realize flexible microscale sensors. The method involves drop‐casting a graphene film on a prepatterned polydimethylsiloxane (PDMS) surface containing negative features by graphene suspensions, applying Scotch tape to remove the excess graphene from the nonpatterned areas of the PDMS surface, and then transferring the patterned graphene from the inside of the negative features at the PDMS surface onto a target tape. The feature size of transferred graphene patterns on the final tape is as small as a few micrometers. This method is easy to implement, but does not require the use of expensive equipment, except for needing a PDMS substrate containing negative features. This method has a high versatility in producing micropatterns of graphene‐based nanomaterials on different types of adhesive tape. For the purpose of application demonstration, flexible mechanical sensors and sensor arrays, smart gloves, and plant leaf sensors on tapes to realize real‐time monitoring of important signals indicating human motion and plant water transport behavior have been developed. This technology will open a new route for low‐cost, scalable, and roll‐to‐roll production of graphene‐based sensors on tape.