The influence of target torque and torque build-up time on physical stress in right angle nutrunner operation

This study used a computer-controlled electric right angle nutrunner to investigate the relative effects of different power hand tool and process parameters on operator muscular exertions, handle stability and subjective ratings of perceived exertion. Target torque (25, 40 and 55 Nm), torque build-up time (35, 150, 300, 500 and 900 ms), and workstation orientation (horizontal and vertical) were studied. Dependent variables included EMG activity of the finger flexors, biceps, and triceps, handle velocity and displacement, work done on the tool-hand system and power involved in doing work, subjective ratings of perceived exertion, and task acceptance. Six inexperienced subjects (three females and three males) participated. Ten replications were performed for each combination of experimental conditions. The consequences of increasing the torque reaction force were greater handle instability and perceived exertion. The effect of torque build-up time on handle kinematics, muscular activity and perceived exertion was not monotonic. Among five build-up times tested, the hand was most unstable (greater peak handle velocity and power against the operator) for a 150 ms build-up time. Greater peak handle displacement, total work against the operator and average EMG were observed for 150 and 300 ms build-up times than for other build-up time conditions. Integrated EMG and EMG latency significantly increased as build-up time increased. Average EMG latency between the onset of EMG burst and the onset of torque build-up was 40 ms for a 35 ms build-up time and 330 ms for a 900 ms build-up time. Subjective ratings of perceived exertion were the least when torque build-up time was 35 ms, however greater peak torque variance was associated with this condition.     

Dynamic biomechanical model of the hand and arm in pistol grip power handtool usage

The study considers the dynamic nature of the human power handtool operator as a single degree-of-freedom mechanical torsional system. The hand and arm are, therefore, represented as a single mass, spring and damper. The values of these mechanical elements are dependent on the posture used and operator. The apparatus used to quantify these elements measured the free vibration frequency and amplitude decay of a known system due to the external loading of the hand and arm. Twenty-five subjects participated in the investigation. A full factorial experiment tested the effects on the three passive elements in the model when operators exerted maximum effort for gender, horizontal distance (30, 60, 90 cm), and vertical distance (55, 93, 142 190 cm) from the ankles to the handle. The results show that the spring element stiffness and mass moment of inertia changed by 20.6 and 44.5% respectively with vertical location (p<0.01), and 23.6 and 41.2% respectively with horizontal location (p<0.01). Mass moment of inertia and viscous damping for males were 31.1 and 38.5% respectively greater than for females (p<0.01). Tool handle displacement and hand force during torque build-up can, therefore, be predicted based on this model for different tool and workplace parameters. The biomechanical model was validated by recalling five subjects and having them operate a power handtool for varying horizontal distances (30, 60, 90 cm), vertical distances (55, 93, 142 cm), and two torque build-up times (70, 200 ms). Tool reaction displacement was measured using a 3D-motion analysis system. The predictions were closely correlated with these measurements (R = 0.88), although the model underpredicted the response by 27%. This was anticipated since it was unlikely that operators used maximal exertions for operating the tools.     

Short-term changes in upper extremity dynamic mechanical response parameters following power hand tool use

Dynamic mechanical response parameters (stiffness, damping and effective mass), physiological properties (strength and swelling) and symptoms of the upper limb were measured before power tool operation, immediately following and 24 h after power tool operation. Tool factors, including peak torque (3 Nm and 9 Nm) and torque build-up time (50 ms and 250 ms), were controlled in a full factorial design. Twenty-nine inexperienced power hand tool users were randomly assigned to one of four conditions and operated a pistol grip nutrunner four times per min for 1 h in the laboratory. Isometric strength decreased immediately following tool use (15%) (p < 0.01) and 24 h later (9%) (p < 0.05). Mechanical parameters of stiffness (p < 0.05) and effective mass (p < 0.05) were affected by build-up time. An average decrease in stiffness (43%) and effective mass (57%) of the upper limb was observed immediately following pistol grip nutrunner operation for the long (250 ms) build-up time. A previously developed biomechanical model was used to estimate handle force and displacement associated with the tool factors in the experiment. The conditions associated with the greatest predicted handle force and displacement had the greatest decrease in mechanical stiffness and effective mass, and the greatest increase in localized discomfort.     

A video-based system for acquiring biomechanical data synchronizedwith arbitrary events and activities

A video-based data acquisition and interactive multimedia data extraction system are described for measuring and synchronizing large quantities of biomechanical analog data with arbitrary events and activities. Analog signals from up to 32 channels are digitized, frequency-shift key (FSK) coded, and recorded directly onto the audio tracks of a video tape in synchronization with the video information. The data acquisition system includes an A/D converter that digitizes up to 16 multiplexed channels of 8-b data at a fixed sample rate between 60 and 960 Hz, and an FSK modem that transfers the data onto one of two VHS high fidelity (20 Hz-20 kHz bandwidth) audio tracks. Twenty megabytes of digitized data and time codes, along with associated video and normal audio are contained on a conventional 120-min video tape. An analyst interactively reviews the video tape off-line using a computer-controlled VCR and identifies specific events that divide arbitrary activities into time segments. The computer automatically extracts the biomechanical data corresponding to each time segment for further processing or analysis. This system is useful for ergonomics, gait analysis, sports medicine, sleep laboratory, biomechanics, or any application where complex visual events are synchronized with low-frequency analog data     

Mental workload during brain-computer interface training

It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. PRACTITIONER SUMMARY: Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.     

A new method for estimating hand internal loads from external force measurements

This study examines using force vectors measured using a directional strain gauge grip dynamometer for estimating finger flexor tendon tension. Fifty-three right-handed participants (25 males and 28 females) grasped varying-sized instrumented cylinders (2.54, 3.81, 5.08, 6.35 and 7.62 cm diameter) using a maximal voluntary power grip. The grip force vector magnitude and direction, referenced to the third metacarpal, was resolved by taking two orthogonal grip force measurements. A simple biomechanical model incorporating the flexor tendons was used to estimate long finger tendon tension during power grip. The flexor digitorum superficialis and the flexor digitorum profundus were assumed to create a moment about the metacarpal phalange (MCP) joint that equals and counteracts a moment around the MCP joint measured externally by the dynamometer. The model revealed that tendon tension increased by 130% from the smallest size handle to the largest, even though grip force magnitude decreased 36% for the same handles. The study demonstrates that grip force vectors may be useful for estimating internal hand forces.     

Gain effects on performance using a head-controlled computer input device.

The purpose of this study was to use a Fitts' task to (1) determine how control-display gain influences performance using a head-controlled computer input device; (2) compare relative sensitivity to gain and optimal gain between head control and hand/arm control; and (3) investigate control-display gain interactions with other task factors including target width, movement amplitude and direction. The task was a discrete target acquisition task using circular targets of 2.9 mm, 8.1 mm, and 23.5 mm, movement amplitudes of 24.3 mm and 61.7 mm, and eight radial directions including 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, and 315 degrees. Each device was operated at four gain levels. Ten subjects participated. The results indicated that gain had a significant effect on movement time for both types of pointing devices and exhibited local minimums. Discrete target acquisition at all gains was aptly described using Fitts' Law for both input devices. The mouse gain resulting in minimum movement time and RMS cursor deviation was between 1.0 and 2.0. The minimum movement time and RMS cursor deviation for the head-controlled pointer occurred at a gain between 0.3 and 0.6. Average movement time at the optimal head-controlled pointer gain had a slope of 169 ms/bit and was more than 76% greater than at the optimal mouse gain with a slope of 135 ms/bit. In addition, average RMS displacement was more than 27% greater for the head-controlled pointer at its optimal gain setting than for the mouse. Gain had the greatest effect for small target widths and long movement amplitudes using the head-controlled pointer. Average movement time increased 37% when increasing the head-controlled pointer gain from 0.6 to 1.2 for the small target width, but only increased 0.3% when increasing gain for the large target width. Average movement time also increased 12% when decreasing the head-controlled pointer gain from 0.3 to 0.15 for the long movement amplitude, but decreased 0.3% when decreasing gain for the short movement amplitude.     

Forces associated with pneumatic power screwdriver operation: statics and dynamics

The statics and dynamics of pneumatic power screwdriver operation were investigated in the context of predicting forces acting against the human operator. A static force model is described in the paper, based on tool geometry, mass, orientation in space, feed force, torque build up, and stall torque. Three common power hand tool shapes are considered, including pistol grip, right angle, and in-line. The static model estimates handle force needed to support a power nutrunner when it acts against the tightened fastener with a constant torque. A system of equations for static force and moment equilibrium conditions are established, and the resultant handle force (resolved in orthogonal directions) is calculated in matrix form. A dynamic model is formulated to describe pneumatic motor torque build-up characteristics dependent on threaded fastener joint hardness. Six pneumatic tools were tested to validate the deterministic model. The average torque prediction error was 6.6% (SD = 5.4%) and the average handle force prediction error was 6.7% (SD = 6.4%) for a medium-soft threaded fastener joint. The average torque prediction error was 5.2% (SD = 5.3%) and the average handle force prediction error was 3.6% (SD = 3.2%) for a hard threaded fastener joint. Use of these equations for estimating handle forces based on passive mechanical elements representing the human operator is also described. These models together should be useful for considering tool handle force in the selection and design of power screwdrivers, particularly for minimizing handle forces in the prevention of injuries and work related musculoskeletal disorders.     

A 16-channel 8-parameter waveform electrotactile stimulation system

We have developed a general-purpose electrotactile (electrocutaneous) stimulation system as a research tool for studying psychophysiological performance associated with various stimulation waveforms. An experimenter-defined command file specifies the stimulation current and waveform of each of the 16 channels. The system provides burst onset delay of 0-20 ms, phase current of 0-50 mA, interphase interval of 0-1000 microseconds, number of pulses per burst from 1-100, pulse repetition rate of 0.1-25 kHz, phase width of 2-1000 microseconds, and functionally-monophasic pulses (with zero dc current) or balanced-biphasic pulses (with equal positive and negative phases). The system automatically delivers the desired stimulation, prompts the subject for responses, and then logs subject responses. Key features of the system are 1) very flexible choice of bursts of pulsatile waveforms, 2) real-time control of all of the waveform parameters as mathematical functions of external analog inputs, and 3) high-performance electrode-driver circuitry.