Task Constraints Research Articles

Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.

Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture data as a reference and for computing virtual inertial data such that the desired solution was known exactly. We generated the inertial tracking simulations by formulating optimal control problems that tracked virtual acceleration and angular velocity while minimizing effort without requiring a task constraint or an initial state. To evaluate the proposed approach, we reconstructed three trials each of straight running, curved running, and a v-cut of 10 participants. We compared the estimated inertial signals and biomechanical variables of the marker and inertial tracking simulations. The inertial data was tracked closely, resulting in low mean root mean squared deviations for pelvis translation (≤20.2mm), angles (≤1.8 deg), ground reaction forces (≤1.1 BW%), joint moments (≤0.1 BWBH%), and muscle forces (≤5.4 BW%) and high mean coefficients of multiple correlation for all biomechanical variables . Accordingly, our results showed that optimal control simulations tracking 3D inertial data could reconstruct the kinematics and kinetics of individual trials of all running motions. The simulations led to mutually and dynamically consistent kinematics and kinetics, which allows researching causal chains, for example, to analyze anterior cruciate ligament injury prevention. Our work proved the feasibility of the approach using virtual inertial data. When using the approach in the future with measured data, the sensor location and alignment on the segment must be estimated, and soft-tissue artifacts are potential error sources. Nevertheless, we demonstrated that optimal control simulation tracking inertial data is highly promising for estimating 3D kinematics and kinetics for a comprehensive biomechanical analysis.

LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multi-Core Systems

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this paper presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this paper, the number of tolerable faults for each execution section of a task, and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the QoS of low-criticality tasks, and managing energy consumption with an average of 29.49%, and 32.78%, respectively.

PRogramAR: Augmented Reality End-User Robot Programming

The field of end-user robot programming seeks to develop methods that empower non-expert programmers to task and modify robot operations. In doing so, researchers may enhance robot flexibility and broaden the scope of robot deployments into the real world. We introduce PRogramAR (Programming Robots using Augmented Reality), a novel end-user robot programming system that combines the intuitive visual feedback of augmented reality (AR) with the simplistic and responsive paradigm of trigger-action programming (TAP) to facilitate human-robot collaboration. Through PRogramAR, users are able to rapidly author task rules and desired reactive robot behaviors, while specifying task constraints and observing program feedback contextualized directly in the real world. PRogramAR provides feedback by simulating the robot’s intended behavior and providing instant evaluation of TAP rule executability to help end users better understand and debug their programs during development. In a system validation, 17 end users ranging from ages 18 to 83 used PRogramAR to program a robot to assist them in completing three collaborative tasks. Our results demonstrate how merging the benefits of AR and TAP using elements from prior robot programming research into a single novel system can successfully enhance the robot programming process for non-expert users.

Bagging in Hidden Semi-Markov Model for handwriting robot trajectory generation

Handwriting robots as an application of Imitation Learning (IL). However, most methods have poor accuracy of trajectory generation under task constraints, and models are less robust to changes in demonstration data. This paper proposes an IL algorithm named Bagging in Hidden Semi-Markov Model (BHSMM). The demonstration data is first divided into several sub-datasets, and each sub-dataset is encoded into several basic learning models by Hidden Semi-Markov Models (HSMM). Then the relationship between the task constraint points and the basic learning models is used to derive the weights. Finally, the trajectories adapted to the task constraints are generated based on the weights. We conducted experiments on the handwritten dataset LASA and compared the accuracy error with the original HSMM method. The results show that the BHSMM can generate trajectories that satisfy the position and velocity constraints and is more robust to changes in the demonstration data than the HSMM. In addition, satisfactory results are obtained in trajectory generation for real robot handwriting.

Is Task Analysis a Contributing Factor to Performance Analysis?

Abstract Introduction. With the evolution of sport, the level of athletes is increasing. To be able to win it is necessary to understand what factors influence performance in competition and training. Sports performance analysis has evolved in accordance with data collection in competition practice, with performance indicators related to actions connected to performance outcome. Material and Methods. We intend to verify the importance of task analysis in the performance analysis procedures. The review starts with task definition, then task analysis procedures and sports science application to finish with examples of performance analysis with task analysis. Results. Each task has one or more goals with actions to be carried out and with task, performer, and environment constraints that influence it. Task analysis is a methodology to have information about technique, productivity and safety that allows developing training programmes. Sports task analysis has a preparatory phase, a description (phase analysis), assessment, and an intervention. Performance analysis was based on biomechanical and notational analysis including performance indicators and afterwards reinforced with the ecological dynamics approach with an emphasis on the performer-environment relationship. Similarities between actions from different sports could be observed with performance analysis using task analysis (phase analysis with description of the segmental actions and mechanical principles). Conclusions. Performance task analysis could support evidence-based training programmes with a replication of key features and performance indicators in dry-land training tasks. Future research should identify task constraints and performance indicators with technological devices and make performance analysis more coach friendly.

Chimpanzees demonstrate a behavioural signature of human joint action

The strength of human society can largely be attributed to the tendency to work together to achieve outcomes that are not possible alone. Effective social coordination benefits from mentally representing a partner's actions. Specifically, humans optimize social coordination by forming internal action models adapted to joint rather than individual task demands. To what extent do humans share the cognitive mechanisms that support optimal human coordination and collaboration with other species? An ecologically inspired joint handover-to-retrieve task was systematically manipulated across several experiments to assess whether joint action planning in chimpanzees reflects similar patterns to humans. Chimpanzees' chosen handover locations shifted towards the location of the experimenter's free or unobstructed hand, suggesting they represent the constraints of the joint task even though their individual half of the task was unobstructed. These findings indicate that chimpanzees and humans may share common cognitive mechanisms or predispositions that support joint action.

Impact of motor task conditions on end-point kinematics and trunk movements during goal-directed arm reach

Task conditions significantly impact human motor control. We investigated how task type, difficulty, and constraints influence the kinematics of goal-directed arm reaching. Non-disabled young adults performed two distinct goal-directed arm reaching tasks: pointing and picking up an object with chopsticks. These tasks were carried out under various conditions, including constrained and unconstrained elbow extension and two different task difficulties. We collected kinematic data using a 3-D motion capture system and analyzed the effects of different task conditions on kinematic variables using linear mixed-effects regression analysis. Our findings revealed statistically significant differences in kinematics between the two tasks. Arm reaching during the picking-up task was slower and exhibited jerkier movements compared to the pointing task. Additionally, when arm reaching was performed with constrained elbow extension, it led to slower and jerkier movements, with an increased involvement of trunk movements compared to the unconstrained condition. These findings show that complex manipulative motor tasks requiring higher hand dexterity necessitate feedback-based control of arm reaching, but simple pointing tasks requiring less hand dexterity do not. In conclusion, our study sheds light on the influence of task conditions on goal-directed arm reaching kinematics and provides valuable insights into the motor control strategies involved in different tasks.

Finding constraints to foster creativity in language learning tasks: An autoethnographic approach

AbstractTeachers often repeat language learning tasks and materials with students year after year. Although some tasks do not render satisfactory outcomes, students' performances can serve as valuable historical autoethnographic data that teachers can reflect on to improve their teaching materials and language learning tasks. In this article, using autoethnography as a pedagogical tool, I share my thinking and experiences regarding one task type (the simile task), which I have used with students over the years to develop creative language use (language creativity). I reflect on various similes produced and attempt to discover how to redesign the somewhat imperfect task to optimize creative language use. In particular, I try to find new constraints that can be set up to funnel the creative behaviour of the teacher and students. Before promoting students' creativity, teachers must nurture their creativity under various self‐imposed constraints. Teachers' personal experience is a valuable source when implementing creative language teaching. This article shows two task constraints that can be set up to stimulate creativity in language learning tasks: exclusionary constraints (avoiding confirmation‐based salient patterns) and focusing constraints (requiring the use of violation‐based salient patterns). It also shows how we can identify micro‐constraints to be excluded or focused on.

Promoting Exploration During Learning: Effect of Imposed and Self-Controlled Practice Schedules on Learners’ Behavioral Flexibility

ABSTRACT Purpose: Enriching learners’ motor repertoires in a complex pluri-articular task, such as climbing, could help learners’ adaptation to various sets of task constraints. Promoting exploration with variable practice conditions is one solution recurrently proposed. However, recent studies have shown that a too elevated exploration-exploitation ratio during practice could impair learning. A proposed solution is to give learners some control over their practice schedule, which appeared to better respect the individual learning dynamic in comparison to the usual experimenter-imposed practice schedule. Thus, the aim of this study is to investigate whether giving learners the possibility of controlling when to confront to new climbing routes would result in greater flexibility in their motor repertoire compared to giving them an imposed schedule of climbing routes or a constant practice condition. Method: Participants were assigned to either a constant practice group (CPG), an imposed-variability group (IVG) or a self-controlled variability group (SVG) to carry out a climbing task. To assess participants’ behavioral flexibility, a scanning procedure was conceived by manipulating the route design and the instructions. Results: Participants showed an initial lack of flexibility as they strongly relied on a single coordination pattern. At posttest and retention, the three groups more frequently used a new hand coordination pattern and more often showed coordination patterns associated with high climbing fluency. Results suggest that the individualized rate of exploration in the self-controlled practice condition may have helped the learners improve their flexibility, whereas forcing exploration did not seem more beneficial than constant practice in this complex pluri-articular task.

An ecological approach for skill development and performance in soccer goalkeeper training: Empirical evidence and coaching applications

ABSTRACT Ecological approaches in sport consider that athletes adapt to properties of the task and the surrounding environment. Thus, task and environment are key constraints of performance. Yet, the influence of task and environmental constraints on athletes’ performance needs empirical examination, especially in sport-specific contexts such as soccer goalkeeping. This study aimed to examine if and how task and environmental constraints influenced goalkeepers (GKs’) performances. We monitored performance coefficients of two professional female GKs across 13 training tasks that varied based on 9 constraints, referring to both interactions among athletes and properties of the surrounding landscape. Results showed that constraints explain ~ 47% of the observed variability in GKs’ performances. Numerical complexity (i.e., the potential interactions between athletes) showed a major influence on performance, which indicates that number of interactions among athletes may constrain GKs’ perceived opportunities for action. Field dimensions and landscape representativity (including elements such as penalty area(s), target goal(s) and constraints for shooting) showed positive relationships with performance, supporting that training designs retaining closer proximity to the game may benefit GKs’ performances. Overall, results supported that athlete-environment couplings could be understood as a multifactorial model and hence, a combination of task constraints are necessary for designing effective learning environments.

How do paediatric physical therapists teach motor skills to children with Developmental Coordination Disorder? An interview study.

When teaching motor skills, paediatric physical therapists (PPTs) use various motor learning strategies (MLSs), adapting these to suit the individual child and the task being practised. Knowledge about the clinical decision-making process of PPTs in choosing and adapting MLSs when treating children with Developmental Coordination Disorder (DCD) is currently lacking. Therefore, this qualitative study aimed to explore PPTs' use of MLSs when teaching motor skills to children with DCD. Semi-structured individual and group interviews were conducted with PPTs with a wide range of experience in treating children with DCD. A conventional content analysis approach was used where all transcripts were open-coded by two reviewers independently. Categories and themes were discussed within the research group. Data were collected until saturation was reached. Twenty-six PPTs (median age: 49 years; range: 26-66) participated in 12 individual interviews and two focus-group interviews. Six themes were identified: (1) PPTs treated children in a tailor-made way; (2) PPTs' teaching style was either more indirect or direct; (3) PPTs used various strategies to improve children's motivation; (4) PPTs had reached the optimal level of practice when children were challenged; (5) PPTs gave special attention to automatization and transfer during treatment; and (6) PPTs considered task complexity when choosing MLSs, which appeared determined by task constraints, environmental demands, child and therapist characteristics. PPTs' clinical decision-making processes in choosing MLSs appeared strongly influenced by therapist characteristics like knowledge and experience, resulting in large variation in the use of MLSs and teaching styles to enhance motivation, automatization, and transfer. This study indicates the importance of the level of education on using MLSs to teach children motor skills, and clinical decision-making. Future research should focus on implementing this knowledge into daily practice.

Neural Optimal Control for Constrained Visual Servoing via Learning From Demonstration

This paper proposes a novel optimal control scheme for constrained image based visual servoing of a robot manipulator. For a robot manipulator with an eye-on-hand configuration, visibility constraint is an essential requirement to avoid servo failure, while robot’s actuator limits must also be satisfied. To ensure this, the constraints are modelled implicitly via learning the task and defining safe regions using expert human demonstrations via mixture of Dynamic Movement Primitives (DMPs). The visual servoing problem is then formulated as a closed-loop optimal control problem using these constraint model where a desired target (possibly time-varying) is obtained by acting upon the feedback from the real-time visual sensors. The visual servo control loop consists of a single network adaptive critic optimal tracking control scheme whose weights are tuned using Lyapunov stability criteria. The stability and the performance of the proposed control scheme is shown theoretically via Lyapunov approach and also verified experimentally using a seven degree of freedom (DOF) Franka Emika and six DOF Universal Robot (UR) 10 manipulator. The approach is also demonstrated on a use case scenarios in mock-up convenience store and warehouse setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The applications of robots in busy warehouses, healthcare sectors and convenience store, has a big societal impact. The scenarios in these real-world problems often consist of a dynamic environment. Therefore, sensor-based localization and planning, along with satisfying robot and task constraints are needed. These naturally adds up to the programming costs in addition to the robot platform and actuation. However, modern robots need intuitive and easy programming for more pervasive in a practical societal application. In our proposed method, this sensor-based planning with environmental constraints is modelled implicitly using the Programming by Demonstration framework. The user needs to catch the robot arm by his hand and teach the task at hand, and the framework captures the task and robot constraints while generalizing to new goals. This modelling, along with cost optimal controller, generates real-time constraint aware robot manipulation trajectories for the demonstrated task in dynamic environments.

Adaptation of the inferior temporal neurons and efficient visual processing.

Numerous studies examining the responses of individual neurons in the inferior temporal (IT) cortex have revealed their characteristics such as two-dimensional or three-dimensional shape tuning, objects, or category selectivity. While these basic selectivities have been studied assuming that their response to stimuli is relatively stable, physiological experiments have revealed that the responsiveness of IT neurons also depends on visual experience. The activity changes of IT neurons occur over various time ranges; among these, repetition suppression (RS), in particular, is robustly observed in IT neurons without any behavioral or task constraints. I observed a similar phenomenon in the ventral visual neurons in macaque monkeys while they engaged in free viewing and actively fixated on one consistent object multiple times. This observation indicates that the phenomenon also occurs in natural situations during which the subject actively views stimuli without forced fixation, suggesting that this phenomenon is an everyday occurrence and widespread across regions of the visual system, making it a default process for visual neurons. Such short-term activity modulation may be a key to understanding the visual system; however, the circuit mechanism and the biological significance of RS remain unclear. Thus, in this review, I summarize the observed modulation types in IT neurons and the known properties of RS. Subsequently, I discuss adaptation in vision, including concepts such as efficient and predictive coding, as well as the relationship between adaptation and psychophysical aftereffects. Finally, I discuss some conceptual implications of this phenomenon as well as the circuit mechanisms and the models that may explain adaptation as a fundamental aspect of visual processing.

How coaches design small-sided games in rugby union: a practice-based review

Background: Game-based training (GBT) is a holistic method for improving performance in team sports. Task constraints and design parameters can affect training effectiveness. The evidence on how small-sided game (SSG) design in rugby union (RU) influences outcome variables is lagging. No study has investigated the contemporary SSG practices of RU practitioners in different performance contexts. Methods An anonymous online survey with twenty-four questions was distributed to RU training staff six times worldwide in one year through multiple e-platforms. A frequency analysis was used to assess responses and contrast it with the literature. Results: 115 surveys were recorded. The dominant characteristics were &gt;5 y experience (66% of respondents), head coach position (53%), New Zealand-based (77%), coaching male players (69%) on the local/school level (68%). Higher-level practitioners applied larger SSG formats with shorter duration, using more extreme pitch sizes and less tackling, for conditioning purposes primarily. Lower-level practitioners targeted fun and technique. Smaller game formats and tackling were used more in male and youth players, and on the lower levels. Further observed differences relate to practitioner role, experience, and geographical location. Conclusion: Practitioners design SSG differently depending on their coaching background, target population, competition level, and geographical location. Practitioner education on GBT would optimise RU training practice. This study provides performance-contextual insights.

CMBEE: A constraint-based multi-task learning framework for biomedical event extraction

Objective:Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. Material and Methods:We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. Results:Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). Conclusions:The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.

An Intelligent Obstacle Avoidance Method for Six Degree of Freedom Robotic Arm Based on Assembly Sequence Planning

Abstract Autonomous operation planning of robotic arms under complex task constraints is still a difficult problem in the current research in this direction, and the existing operation planning methods are facing challenges and urgently need to break through the technical bottleneck. This paper proposes an improved RRT algorithm for six-degree-of-freedom robotic arms, which can be used for obstacle avoidance path planning. Based on the assembly sequence planning method, combining the advantages of RRT and RRT-connect algorithm, applying the idea of target sampling to strengthen the convergence of the algorithm to the target point search, introducing the greedy algorithm to improve the efficiency of the algorithm, combining with the five times of the B-spline interpolation on the path of the smoothing optimization, to shorten the planning path time and length. The Robotics Toolbox toolkit in MATLAB is used to count the time, number of samples, and path length required for the planning of RRT and the improved RRT algorithm, and the dynamic planning library is used to configure the algorithm and verify the path planning effect of the improved algorithm. Compared with the traditional RRT algorithm, using the proposed SDPS-RRT Connect algorithm for six-degree-of-freedom robotic arm path planning, the minimum distance of the connecting rod from the obstacle is 51.4948cm, which is greater than the standard safety distance of 45cm, which verifies the feasibility of the algorithm in practical applications.

Influence of daily physical activity on fine motor skills of adults around a Fitts task.

Achieving our daily tasks depends on the speed-accuracy conflict. Physical activity plays a role in the development of our motor skills. However, the relationship between physical activity level (PAL) and fine motor skills remains largely unexplored.

The Impact of the Gestational Period and Weight on Individual, Task and Environmental Constraints as Related to Perceived Confidence when Completing Ambulatory Tasks in Pregnant Women

Background: Pregnancy and the physical, sensory, perceptual, and motor changes that coincide with pregnancy can affect a person’s ability to perform basic activities of daily living (ADL’s) that involve ambulation. Previous research suggested that weight and gestational age (trimester) are factors that impact woman’s ability to ambulate through daily tasks, however, these issues require further investigation. Newell's model of constraints was used as a framework to shape the questionnaire investigating the ability to perform different ambulatory activities across various constraints. Thus, the purpose of this study was to examine whether variables such as pre-pregnancy weight (under vs. over 155 pounds) and trimester (before vs. after 26 weeks) had an impact on the ability and perception to perform different tasks requiring ambulation. Methods: Twenty pregnant or postpartum women (M = 29.7 years; SD = 3.6) were recruited and asked to complete a 15-item online survey exploring their abilities to complete a variety of daily tasks involving balance. Also, open ended questions were posed to gain qualitative insight into their experiences. A series of independent samples T-tests were used to analyse the data. Findings: The results revealed predominantly no statistical differences between the levels of the variables manipulated. At the descriptive level the women indicated that they were relatively comfortable performing the different tasks. However, the qualitative responses suggested that although they were capable, they expended a substantial amount of energy to avoid falling, resulting in fatigue and soreness. Also, they experienced overall mental anguish and feelings of being overwhelmed when performing even simple tasks involving gait. Conclusion: Overall, it was surprising that weight and gestational age, which intuitively should have an impact, did not have a more pronounced effect on the women’s confidence in performing ambulation tasks. Nevertheless, qualitative responses confirmed the challenging effects of pregnancy on the way women engage in activities of daily living.

Dynamic gait stability in children with and without Down syndrome during overground walking

BackgroundChallenging children with Down syndrome to walk fast and with external ankle load has demonstrated acute adjustments, such as increased step length and decreased step width, and improved joint stability. However, it is unknown to what extent these task constraints affect gait stability. Assessing dynamic gait stability through margin-of-stability not only quantifies adjustments related to balance control, but also informs safety considerations. MethodsTwenty-four children with and without Down syndrome participated in this study. Subjects walked overground at two speed conditions: self-selected (normal) and as fast as possible (fast); and two ankle load conditions: no load and ankle load of 2% body mass. We assessed margin-of-stability in the anteroposterior and mediolateral directions, separately, at three gait events of toe-off, mid-swing, and heel strike. FindingsChildren with Down syndrome walked with greater mediolateral margin-of-stability than typically developing children. Children with Down syndrome demonstrated less anteroposterior margin-of-stability than typically developing children only before heel-strike at normal speed but increased anteroposterior margin-of-stability at fast speed. The ankle load increased the mediolateral margin-of-stability in both groups but did not impact the anteroposterior margin-of-stability. In addition, children with Down syndrome took shorter and wider steps for more lateral placement of mediolateral margin-of-stability. InterpretationChildren with Down syndrome were capable of adjusting their margin-of-stability for fast walking and ankle-load conditions. However, children with Down syndrome walked with less mediolateral stability and anteroposterior mobility than typically developing children. Children with Down syndrome overcompensate the mediolateral foot placement to recapture their margin-of-stability and maintain gait stability.

A Comparative Analysis of Optimal and Biomechanical Torque Control Strategies for Powered Knee Exoskeletons in Squat Lifting

Abstract Exoskeletons have the ability to aid humans in physically demanding and injury-prone activities, such as lifting loads while squatting. However, despite their immense potential, the control of powered exoskeletons remains a persistent challenge. In this study, we first predict the human lifting motion and knee joint torque using an inverse dynamics optimization formulation with a two-dimensional (2D) human skeletal model. The design variables are human joint angle profiles. The normalized human joint torque squared is minimized subject to physical and lifting task constraints. After that, the biomechanical assistive knee exoskeleton torque is obtained by scaling the predicted human knee joint torque. Second, we also present a 2D human skeletal model with a powered knee exoskeleton for predicting the optimal assistive torque and lifting motion. The design variables are human joint angle profiles and exoskeleton motor current profiles. Then, the biomechanical and optimal exoskeleton torques are implemented in a powered knee exoskeleton in real-time to provide external assistance in human lifting motion. Finally, the biomechanical and optimal assistive exoskeleton torque controls for lifting are compared. It is observed that both control methods have a significant impact on reducing muscle activations for the specific muscle groups compared to the cases without the exoskeleton. Especially, peak activations of erector spinae and rectus femoris muscles are reduced by 57.79% and 47.26% with biomechanical assistive torque. Likewise, vastus medialis and vastus lateralis activations drop by 46.82% and 52.24% with optimal assistive torque.