Book•
Biomechanics and Motor Control of Human Movement
01 May 1990-
TL;DR: The Fourth Edition of Biomechanics as an Interdiscipline: A Review of the Fourth Edition focuses on biomechanical Electromyography, with a focus on the relationship between Electromyogram and Biomechinical Variables.
Abstract: Preface to the Fourth Edition. 1 Biomechanics as an Interdiscipline. 1.0 Introduction. 1.1 Measurement, Description, Analysis, and Assessment. 1.2 Biomechanics and its Relationship with Physiology and Anatomy. 1.3 Scope of the Textbook. 1.4 References. 2 Signal Processing. 2.0 Introduction. 2.1 Auto- and Cross-Correlation Analyses. 2.2 Frequency Analysis. 2.3 Ensemble Averaging of Repetitive Waveforms. 2.4 References. 3 Kinematics. 3.0 Historical Development and Complexity of Problem. 3.1 Kinematic Conventions. 3.2 Direct Measurement Techniques. 3.3 Imaging Measurement Techniques. 3.4 Processing of Raw Kinematic Data. 3.5 Calculation of Other Kinematic Variables. 3.6 Problems Based on Kinematic Data. 3.7 References. 4 Anthropometry. 4.0 Scope of Anthropometry in Movement Biomechanics. 4.1 Density, Mass, and Inertial Properties. 4.2 Direct Experimental Measures. 4.3 Muscle Anthropometry. 4.4 Problems Based on Anthropometric Data. 4.5 References. 5 Kinetics: Forces and Moments of Force. 5.0 Biomechanical Models. 5.1 Basic Link-Segment Equations-the Free-Body Diagram. 5.2 Force Transducers and Force Plates. 5.3 Bone-on-Bone Forces During Dynamic Conditions. 5.4 Problems Based on Kinetic and Kinematic Data. 5.5 References. 6 Mechanical Work, Energy, and Power. 6.0 Introduction. 6.1 Efficiency. 6.2 Forms of Energy Storage. 6.3 Calculation of Internal and External Work. 6.4 Power Balances at Joints and Within Segments. 6.5 Problems Based on Kinetic and Kinematic Data. 6.6 References. 7 Three-Dimensional Kinematics and Kinetics. 7.0 Introduction. 7.1 Axes Systems. 7.2 Marker and Anatomical Axes Systems. 7.3 Determination of Segment Angular Velocities and Accelerations. 7.4 Kinetic Analysis of Reaction Forces and Moments. 7.5 Suggested Further Reading. 7.6 References. 8 Synthesis of Human Movement-Forward Solutions. 8.0 Introduction. 8.1 Review of Forward Solution Models. 8.2 Mathematical Formulation. 8.3 System Energy. 8.4 External Forces and Torques. 8.5 Designation of Joints. 8.6 Illustrative Example. 8.7 Conclusions. 8.8 References. 9 Muscle Mechanics. 9.0 Introduction. 9.1 Force-Length Characteristics of Muscles. 9.2 Force-Velocity Characteristics. 9.3 Muscle Modeling. 9.4 References. 10 Kinesiological Electromyography. 10.0 Introduction. 10.1 Electrophysiology of Muscle Contraction. 10.2 Recording of the Electromyogram. 10.3 Processing of the Electromyogram,. 10.4 Relationship between Electromyogram and Biomechanical Variables. 10.5 References. 11 Biomechanical Movement Synergies. 11.0 Introduction. 11.1 The Support Moment Synergy. 11.2 Medial/Lateral and Anterior/Posterior Balance in Standing. 11.3 Dynamic Balance during Walking. 11.4 References. APPENDICES. A. Kinematic, Kinetic, and Energy Data. Figure A.1 Walking Trial-Marker Locations and Mass and Frame Rate Information. Table A.1 Raw Coordinate Data (cm). Table A.2( a ) Filtered Marker Kinematics-Rib Cage and Greater Trochanter (Hip). Table A.2( b ) Filtered Marker Kinematics-Femoral Lateral Epicondyle (Knee) and Head of Fibula. Table A.2( c ) Filtered Marker Kinematics-Lateral Malleolus (Ankle) and Heel. Table A.2( d ) Filtered Marker Kinematics-Fifth Metatarsal and Toe. Table A.3( a ) Linear and Angular Kinematics-Foot. Table A.3( b ) Linear and Angular Kinematics-Leg. Table A.3( c ) Linear and Angular Kinematics-Thigh. Table A.3( d ) Linear and Angular Kinematics-1/2 HAT. Table A.4 Relative Joint Angular Kinematics-Ankle, Knee, and Hip. Table A.5( a ) Reaction Forces and Moments of Force-Ankle and Knee. Table A.5( b ) Reaction Forces and Moments of Force-Hip. Table A.6 Segment Potential, Kinetic, and Total Energies-Foot, Leg, Thigh, and1/2 HAT. Table A.7 Power Generation/Absorption and Transfer-Ankle, Knee, and Hip. B. Units and Definitions Related to Biomechanical and Electromyographical Measurements. Table B.1 Base SI Units. Table B.2 Derived SI Units. Index.
Citations
More filters
TL;DR: Knee motion and knee loading during a landing task are predictors of anterior cruciate ligament injury risk in female athletes and may help develop simpler measures of neuromuscular control that can be used to direct female athletes to more effective, targeted interventions.
Abstract: BackgroundFemale athletes participating in high-risk sports suffer anterior cruciate ligament injury at a 4- to 6-fold greater rate than do male athletes.HypothesisPrescreened female athletes with subsequent anterior cruciate ligament injury will demonstrate decreased neuromuscular control and increased valgus joint loading, predicting anterior cruciate ligament injury risk.Study DesignCohort study; Level of evidence, 2.MethodsThere were 205 female athletes in the high-risk sports of soccer, basketball, and volleyball prospectively measured for neuromuscular control using 3-dimensional kinematics (joint angles) and joint loads using kinetics (joint moments) during a jump-landing task. Analysis of variance as well as linear and logistic regression were used to isolate predictors of risk in athletes who subsequently ruptured the anterior cruciate ligament.ResultsNine athletes had a confirmed anterior cruciate ligament rupture; these 9 had significantly different knee posture and loading compared to the 196 ...
2,997 citations
TL;DR: The inverted pendulum model permitted us to understand the separate roles of the two mechanisms during these critical unbalancing and rebalancing periods and confirmed the critical importance of the hip abductors/adductors in balance during all phases of standing and walking.
2,940 citations
TL;DR: Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.
Abstract: Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
2,303 citations
TL;DR: Increases in explosive muscle strength (contractile RFD and impulse) were observed after heavy-resistance strength training, which could be explained by an enhanced neural drive, as evidenced by marked increases in EMG signal amplitude and rate of EMG rise in the early phase of muscle contraction.
Abstract: The maximal rate of rise in muscle force [rate of force development (RFD)] has important functional consequences as it determines the force that can be generated in the early phase of muscle contraction (0-200 ms). The present study examined the effect of resistance training on contractile RFD and efferent motor outflow ("neural drive") during maximal muscle contraction. Contractile RFD (slope of force-time curve), impulse (time-integrated force), electromyography (EMG) signal amplitude (mean average voltage), and rate of EMG rise (slope of EMG-time curve) were determined (1-kHz sampling rate) during maximal isometric muscle contraction (quadriceps femoris) in 15 male subjects before and after 14 wk of heavy-resistance strength training (38 sessions). Maximal isometric muscle strength [maximal voluntary contraction (MVC)] increased from 291.1 +/- 9.8 to 339.0 +/- 10.2 N. m after training. Contractile RFD determined within time intervals of 30, 50, 100, and 200 ms relative to onset of contraction increased from 1,601 +/- 117 to 2,020 +/- 119 (P < 0.05), 1,802 +/- 121 to 2,201 +/- 106 (P < 0.01), 1,543 +/- 83 to 1,806 +/- 69 (P < 0.01), and 1,141 +/- 45 to 1,363 +/- 44 N. m. s(-1) (P < 0.01), respectively. Corresponding increases were observed in contractile impulse (P < 0.01-0.05). When normalized relative to MVC, contractile RFD increased 15% after training (at zero to one-sixth MVC; P < 0.05). Furthermore, muscle EMG increased (P < 0.01-0.05) 22-143% (mean average voltage) and 41-106% (rate of EMG rise) in the early contraction phase (0-200 ms). In conclusion, increases in explosive muscle strength (contractile RFD and impulse) were observed after heavy-resistance strength training. These findings could be explained by an enhanced neural drive, as evidenced by marked increases in EMG signal amplitude and rate of EMG rise in the early phase of muscle contraction.
1,499 citations
Cites methods from "Biomechanics and Motor Control of H..."
...During later off-line analysis, the strain-gauge signal was smoothed by a digital fourth-order, zero-lag Butterworth filter, by using a cutoff frequency of 15 Hz (48)....
[...]
...During the later process of analysis, the EMG signals were digitally high-pass filtered by using a fourth-order, zero-lag Butterworth filter with a 5-Hz cutoff frequency (48), followed by a moving root-mean-square filter with a time constant of 50 ms (10)....
[...]
TL;DR: A relatively simple control scheme for regulation of upright posture that provides almost instantaneous corrective response and reduces the operating demands on the CNS is proposed.
Abstract: Winter, David A., Aftab E. Patla, Francois Prince, Milad Ishac, and Krystyna Gielo-Perczak. Stiffness control of balance in quiet standing. J. Neurophysiol. 80: 1211–1221, 1998. Our goal was to pro...
1,389 citations
Cites background from "Biomechanics and Motor Control of H..."
...The effective stiffness of the sys- (Winter 1990) ....
[...]
...In the sagittal plane, the ankle plantarflexor/dorsiflexor (Winter 1990)....
[...]
...…pendulum As has been reported in the few papers that have modeled the total body COM with reasonable accuracy (Hasan et al. 1996; Jian et al. 1993; Winter 1990), the COP tracks the COM oscillating where I, Ke , and B are the inertial, spring, and damping constants,either side of it to maintain…...
[...]