Examples of haptic systems and the importance of the haptic sense have been discussed
in the preceding chapters without actually giving an exact idea of the function
of haptic perception. For the design of haptic systems it is vital to have a basic understanding
of characteristic biological parameters, as only these will help to identify
relevant technical requirements. This chapter introduces the most important terminology
and basics for understanding the neurobiology of haptic perception. Please
note that research on haptic perception is far from being complete. As a consequence
this short presentation of complex biological coherences is a well-founded working
hypothesis which will be extended or confuted by further research. In order to perceive
information from our surroundings, man is equipped with five senses: Hearing,
Smelling, Tasting, Sight and Touch. The physiology of senses distinguishes five
sensors and sensory-systems [219] differing from this very popular definition. They
allow a classification in a vocabulary lent from a technical approach to describe
things:
• Thermal sensor for registering the change of temperature especially within the
skin,
• Chemical sensors reacting on odorous or gustatory substances,
• Optical sensors reacting on the impact of photons, especially within the cones
and rods in the retina,
• Pain sensors, also named nociceptors, to identify chemical and physical tissue
damage,
• Mechanical sensors for detecting mechanical tensions and strains e.g. within the
skin or muscles.
The sensory capacity and its importance for haptic perception are valued differently.
The visual sensors register ≈ 10 Mio. bit/s, the sense of touch ≈ 1 Mio.
bit/s and the acoustic sense ≈ 100 kbit/s [18]. The processing of these sensory data
happens within the cerebral cortex. It is structured in functional brain areas. The primary
motor cortex is the physiological location for processing data from the sense of
touch. A visualization of the distribution of body parts on the primary motor cortex
(fig. 3.1) shows a significant portion being used for fingers and hand.
Within the sensorimotor functions the haptic sense has the highest importance.
It consists of a group of mechanical sensors detecting force induced deformations
within tissues in the skin, muscles and joints. As a consequence haptic perception
is the sum of signals from a large number of measurement points distributed among
the human body, consisting of at least 6 types of sensors which can be divided into
two basic groups: Tactile and kinaesthetic sensors (fig. 3.2).
Tactile sensors are located in the outer areas of the skin in exposed positions (e.g.
the fingertips). They react on strains of the skin and are activated either proportionally
to the elongation, to the velocity or to the acceleration. The neuro-pyhsiology
distinguishes between four different types of tactile sensors [236, 219]:
• Rapid-adaption or Fast-Adaption (RA or FA-I) Meissner corpuscles - with velocity
dependent activation.
• Slow-adapting (SA-I and SA-II) Merkel cells and Ruffini-corpuscles with velocitydependent
and elongation proportional activation. They show a lower dynamic
response 1 compared to the Meissner-corpuscles.
• Fast-Adaption (FA-II) Pacinian corpuscles with acceleration-proportional activation.
The distribution of sensors varies within different skin areas (fig. 3.3) and is part
of current research. For example in [193], the existence of Meissner corpuscles has
to be put into question in contrast to established doctrines.
Unlike tactile sensors kinaesthetic sensors are located mainly within muscles,
joints and tendons. They acquire forces affecting whole extremities only. Their dynamic
requirements are reduced as a result of the mechanical low-pass characteristics
of the extremities (their mass, damping and stiffness). Their requirements on
the relative resolution between the smallest perceivable force vs. the maximum de-
tectable force (amplitude-dynamics) can be compared with the group of tactile sensors.
Kinaesthetic sensors can be divided in two groups:
• spindle-stretch-receptors Dynamic Bag fibres and Static Bag fibres placed in parallel
to the muscle fibres.
• spindel-tension-receptors Golgi tendon organ - in serial orientation to the muscle
fibres.
When summarizing all information about biological sensors contributing to haptic
perception, it is interesting to see that nature chooses a design to identify forces
and vibrations, which does not significantly differ from the technical solution of
comparable problems. However comparable technical solutions are older than the
biological understanding of the sense of touch. Therefore it seems likely that with
the given physical constraints only solutions optimized in such a manner are adequate.
Besides dealing with theses sensors as the first part of the haptic perception chain,
in the next step it is necessary to consider a model for the neurological processing
of haptic information, in order to get a feeling for the complexity of the system and
outline components relevant for the design of technical haptic systems.
Figure 3.4 shows a simplified understanding of neuronal subsystems participating
in a task like “grasping a glass of water”. The motivation phase starts with thirst
due to e.g. the body salinity being too high, and with the knowledge about the availability
of a glass of water. As a result a decision is taken to “seize the glass of
water”. Within a programming phase this decision results in a definition of movements
for single extremities and body parts. As a subcomponent each of them has
a position controller, which controls the movement. Feedback is given by the motor
sensors within the joints but also by visual control from a superordinate control loop.
Subordinate to the visual control, a closed-loop circuit with force feedback exists,
enabling the safe and secure holding of the glass based on a maximum force to be
exerted. As an alternative, a feedback loop can be assumed controlling the grasping
force to avoid a slipping through of the glass.
It is remarkable that the analogy to technical control systems can so easily be
drawn. Decision phase, programming phase and processing phase are accepted references
for central-neural structures [219]. The interconnection between position
and force-controller is a direct result from dynamic ranges and measurement errors
unique to the components of the close-loop circuit. The position control loop including
the locomotor system and kinaesthetic sensors shows a dynamic range of
≤ 10Hz [287, 83]. Additionally angle-positioning and absolute position measurement
without a line of sight show large errors (2◦ to 10◦ dependent on the joints
participating [34]). Movements including visual control are much more precise.
The visual perception is able to resolute movements with up to 30 Hz depending
on the illumination level. By the aid of sight a human is able to move to a position
and hold it until immediately before physical contact - which is strictly impossible
with closed eyes. On the other hand tactile sensors show a dynamic range of many
hundred Hertz. This capability combined with high amplitude-dynamics enables
humans to hold even slippery and fragile objects without breaking them.
3.2 Haptic Perception
Knowledge about the performance of haptic perception is essential. for the formulation
of requirements as a basis for system design, For each body part there are
different characteristic values, as the haptic sense is not located in a single organ.
Additionally haptic interaction is always bidirectional, which means - especially
in case of kinaesthetic interaction - that haptic interaction can be mapped only by
considering positions and angles of body parts and forces and torques of mechanical
specifications,. Furthermore haptic perception is also greatly dependent on the
dynamic excitation in a broad frequency range. Last but not least the aspect of multimodality2
has to be considered. Haptically inconspicuous keys and buttons are
considered to be of high quality when they are accompanied by a loud click-sound,
compared to silent buttons with identical haptic properties. As a consequence of
the complexity of effects influencing haptic perception every characteristic value
taken from literature has to be seen in the context of the individual test design and
weighted with the accuracy of the experiment’s layout. The characteristic values presented
within this chapter shall be taken as points for orientation only, and should
be modified and even disapproved of by future experiments.
3.2.1 Psychophysical Concepts
In order to be able to understand characteristic values of haptic perception, a basic
knowledge about some relevant psychophysical concepts is necessary. The definitions
given here are based on G. A. GESCHEIDER [66], recommended to any reader
interested in this subject.
3.2.1.1 Threshold and Difference-Limen
Two fundamental concepts for the analysis of thresholds are distinguished in psychophysics.
On the one hand there is the measurement of thresholds for differential
perception (thresholds of differential sensitivity). On the other hand there are thresholds
for absolute perception (thresholds of absolute sensitivity). All measurement
principles in psychophysics can be categorized according to these two principles.
Additionally the analyzed stimuli differ in their dimensions (e.g. space, time, spectral
3).
The absolute threshold (fig. 3.5) of a stimulus describes the value, from which
a stimulus φ begins to become perceivable.
As another characteristic value the stimulus’ change is relevant, creating a justnoticeable
difference (JND) 4. The stimulus’ change is called difference threshold
or alternatively difference-limen (DL). Consequently the DL means the measurement
of a Δφ being the difference to a stimulus φ0 compared to another stimulus
φ1. The JNDs are numbered discretely as JND being a member of N. The first JND
is the first DL after the absolute threshold; the second JND is that DL following the
sum of the absolute threshold and the first DL (fig. 3.6). To sum up: the JND is the
smallest physiological scale unit of the linearized perception of a physical stimulus
φ .
applied allow conclusions concerning the neuronal processing of stimuli. A classical
method for the analysis of DL is the presentation of a reference stimulus comparing
it to a second stimulus, which is presented to a subject either in an automated way
or manually controlled by the test-person himself (fig. 3.7).
Besides the aspects just mentioned there are others for doing comparable analysis.
On the one hand, there is the aspect of masking with the question: “At which
point will two stimuli dependent on a single parameter be perceived as different?”.
Aspects analyzed frequently for masking a time and spatial dependencies. As a result
the terms of temporal masking and spatial masking have been fixed.
example dynamic masking: The perception of a change in frequency of a mechanical
oscillation of fixed amplitude shall be analyzed. For this purpose two stimuli
are given at the same time to test a subject. The subject is allowed to change the
frequency of one stimulus until he or she detects two independent stimuli. The measure
of the change in frequency Δ f is the value for DL with respect to the reference
stimulus. Results of such a kind of experiments are not always precise and should
be critically analyzed. For example in case of stimulus locations very near to each
other the above experiment can be easily interpreted in a way that only the maximum
amplitude of a summed up signal has been analyzed and not the DL of a frequency
change. To prevent this kind of criticism a careful experiment design should
be done with a series of hypotheses for falsi- and verification. In this case an additional
experiment would be adequate showing a statistically significant difference in
the perception of JND between a summed-up amplitude of two stimuli with identical
frequency compared to a signal with two frequencies.
example temporal masking: A stimulus φ0 with a frequency f is presented for a
long period t. Afterwards stimuli φn, e.g. dependent on frequency, are given. The perception
of those stimuli (e.g. with regard to the absolute threshold) varies dependent
on the prior period t. The measure of this variation is the temporal-masking-effect
of a certain masking frequency f.
example spatial masking: Two stimuli φ0 and φ1, e.g. needles on the skin are given
with a spatial distance d. At a certain distance d both stimuli are perceived independently
from each other. This is a very specific example of spatial masking frequently
used for measuring the resolution of tactile perception. It has therefore been given
its own term: two-point-threshold
Another aspect of analysis is the Successiveness Limen (LM) connected with
the question: “How many stimuli presented consecutively can be perceived?”
Example LM: With the help of a vibratory motor a sequence of stimuli is presented
on a body location. The stimuli vary according to a temporal pattern. The LM is the
temporal pace enabling a correct perception of the sequence.
3.2.1.2 Psychophysical Laws
An important way for presenting DL Δφ is as a value related to a reference stimulus
φ0 according to the formula
In 1834, E.H. WEBER found out, that c is a constant quotient for a specific
perception. In his key experiment he placed weights on the skin and found out, that c
is almost 1/30 . This means that the next higher weight differing from a weight of 200 g is 1/30· 200g+200g = 206.66g. The value c differs significantly between different
stimuli, but the comprehensive coherence according to equation 3.2 (Weber’s law)
seems to be universal for many situations. As a consequence Weber’s law allows
putting different senses and their perception in relation to each other. An exception
is the area of lower stimuli (fig. 3.8a) in the range of absolute thresholds where c
increases significantly.
A modification of Weber’s law
compensates this dependency in the range of absolute thresholds (fig. 3.8b). The
constant a is - identical to c - specific for each sense and as compared to c, quite
low. The physiological reason for a has not finally been determined. A hypothesis
existing assumes it to be a measure for the background noise of the corresponding
receptors.
Some senses, especially the acoustic but also the haptic sense, show a nonlinear
logarithmic dependency on perceived intensity and physical excitation. For the
range of stimuli, for which Weber’s law is valid according to its original formula
(equ. 3.2) a new dependency can be formulated. This dependency, named Fechner’s
law,
Ψ = k logφ........3.4
provides a linearized measureΨ of the perception amplitude.
Today Fechner’s law has mainly a historical significance. In 1975 it was replaced
by S.S. STEVENS, suggesting a law describing the intensity of a stimulus by an
exponential relation:
Ψ = kφ^a (3.5)
This relation is called Power-law and allows comparisons of numerous perceptiondependencies
by a look at its constants a and k. If a = 1, the equation 3.5 gives a
linear dependency. At values for a > 1 the law gives a dependency increasing with
increased stimulus, at a < 1 a damping of the perception with increased stimulus is
resulting. When logarithmizing equation 3.5, an interdependency easy to display on
diagrams with logarithmic axis (fig. 3.9) can be obtained.
logΨ = log k+alogφ (3.6)
with y-axis log k and a slope of a.
Table 3.1 gives an extraction of STEVENS’ published data [242] of the coefficient
a according to equation 3.5.
3.2.1.3 Mean Values and Percentiles
The analysis of psychophysical measures is always laborious due to large variances
in results either between individual subjects or as in certain tests, among specifically
trained test-persons. As a consequence, statistical design and the application of signal
detecting algorithms should be considered for any such experiments. For details
of these procedures, literature of statistical experiment design and [66] is suggested.
For a more general perspective the following remarks should be considered:
Frequently, in psychophysics experimental results follow a Gaussian normal distribution.
This happens with regard to a single person as well as with respect to a
larger number of people. Of course, a Gaussian distribution can be characterized
by a mean-value μ and a standard deviation σ . The mean value defines the measure
where exactly 50% of a given set (e.g. of experiments) are above and below that
value.
For the usage with sets not following a normal distribution, the usage of percentiles
is suggested. Typical examples of their application are anthropometric values
in ergonomics. The x-th percentile gives the point on a scale, where x percent of tests of a given set are below that value. In the exceptional case of a normal
distribution the fiftieth percentile is identical to the mean-value (fig. 3.10).
3.2.2 Frequency Dependency
As mentioned in section 2.2, every kinaesthetic interaction has a tactile component.
We know from the analysis of “grasping a glass of water” in section 3.1 that tactile
components are part of the interaction’s innermost feedback-loop. As a result
the requirements for their dynamic properties are extraordinarily high. This section
discusses the perception thresholds and difference-limens as identified in neurology
and psychophysics from an engineering perspective. It is therefore a preparation
for the identification of requirements for technical systems interfacing the sense of
touch.
The identification of haptic perceptional dynamics can be performed either with
psychophysical methods or with neurological tools. When focussing on the receptors
only, the analysis of tactile and kinaesthetic sensors can be done independently
from each other. Neuronal potential on nerve fibres can be measured via
interventional implanted electrodes, and even positioned during electrode recording
[250, 131].
In [122] several tactile sensory types (compare fig. 3.2 on page 37) have been
analyzed as to their frequency-dependency (dynamics) and their thresholds for the
detection of skin deformation (fig. 3.11). Frequency areas of slow-adapting (SA) and
rapid-adapting (RA) sensors complement and overlap each other. The SA-II sensor
especially affects a range of ≈ 8Hz. According to this study the mean threshold of
the isolated sensors shows a maximum in sensitivity at around ≈ 300Hz with an
elongation of 10μm.
WILKINSON performed a study [287] on thresholds on isolated Golgi-tendons
receptors (fig. 3.12). The results show an almost linear dependency between the
response of receptors in mV and the stimulation in μm over frequency. The relevant
frequency range of these receptors is lower than the range of the tactile receptors of
figure 3.11, especially as the masses and stiffnesses of limbs show distinct low-pass
characteristics. High frequency components of forces and elongations are damped
anyway and therefore the kinaesthetic sensory system does not have to be able to
measure it.
For the design process of haptic systems the focus point does not lie on single
biological receptors but rather on the human’s combined perception resulting
from the sum of all tactile and kinaesthetic sensors. In this area numerous studies
have been performed, three of which are given here showing the range of results.
In 1935, HUGONY already published a study about the perception of oscillations
dependent on frequency of mechanical stimuli [101]. Additionally he quantified
different stimuli-levels which are defined from the absolute perception threshold
to pain-thresholds (fig. 3.13a). To complement this general study TALBOT added
details about the interdependency of the isolated biological sensor and perception
(fig. 3.13b). Both scientists showed that the sensitivity of perception increases to
a frequency of ≈ 200Hz (HUGONY) and ≈ 300Hz (TALBOT). These two studies
along with several others were compiled by HANDWERKER [219] resulting in a
combined curve of haptic perception thresholds (fig. 3.14).
A source for the analysis of haptic perception worth to be recommended can
be found in the publications by GESCHEIDER. He followed a stringent analysismethodology
and discussion of haptic sensory systems. Beginning in 1970 until at
last in 2002 a series of hypotheses and measurements in numerous publications is
documented. Another source worth to be considered is the work by BÉKÉSY [23]
and by JOHANSSON.
Next to the already known dependency of haptic perception on frequency, another
dependency exists connected to the surface area transmitting the mechanical
oscillations: Large areas of force transmission (A > 1cm2) and small areas of force
transmission (A < 1mm2) differ significantly according to their absolute perception
thresholds (fig. 3.15). When focusing on kinaesthetic devices, usually a large area
of force transmission exists. With tactile devices smaller force transmission areas
have to be considered. The perception curve is a combination of the four tactile sensor
types and shows a minimum (point of maximum sensitivity) at ≈ 350Hz. The
frequency-dependency is obvious and undoubted. Only the precise shape and the
exact position of the minimum vary in the range of ≈ 100Hz depending on measurement,
author and publication. Additionally it can be noted that the perception
of very low frequencies below 0.1Hz was not subject to many studies. Typically the
perception curves are assumed to stay constant to lower values from a frequency of
approximately 1Hz.
Besides frequency-dependency there exist two additional dependencies affecting
haptic perception. Ongoing mechanical stimuli result in a reversible desensitization
of receptors. This time dependency is used in [69] to mask single receptor classes in
order to study the part of other receptor classes in overlapping frequency areas. The
time dependency of perception curves ΔK in dB can be approximated according to
ΔK(t) = 12 · (et )12. (3.7)
As a result desensitization happens in a time frame of a second (spectral components
below10 Hz). As a consequence desensitization is not a matter to be necessarily
considered for the design of haptic devices, telemanipulation systems or
simulators due to their large ratio between usage vs. desensitization time frame. A
steady state can be considered for almost all relevant applications. In practical application
this approximation is not necessarily adequate. For example when tactile
devices based on pin- or shear-force systems are used, there is some evidence, according
to the author’s purely subjective observation, that the mentioned effect still
happens after minutes of usage.
The amplitude-resolution (DL) of haptic perception shows a logarithmic dependency
analogue to the visual and acoustic perception. The perception of smallest
changes dependent on frequency with varying base excitation was studied in [68].
Measurements were taken at two frequencies (25Hz, 250Hz) and with white noise.
The approved dependency of DL of the amplitude of the base excitation is nonlinear
with a maximum difference of ≈ +3dB. It is larger for smaller amplitudes of
the base excitation. This allows the conclusion that the Power-Law (section 3.2.1.2,
equ. 3.6) can be used for the description of perception. However its coefficients have
to be identified for every contact situation independently.
3.2.3 Characteristics of Haptic Interaction
Besides the dynamics’ curves in the prior section there are numerous insular values
from experiments documenting the possibilities of haptic interaction. The results
can be divided into two groups. In the table of haptic perception (tab. 3.2) the parameters
from a receptive perspective are summarized. In the table of active movements
(tab. 3.3) border values of the capabilities of the active parts of motor systems
are summarized. The tables are based on a collection by DOERRER [46] and have
been extended by selected additional sources. However, when considering their application,
a very important statement of BURDEA [34] still has to be remembered:
“... that it is dangerous to bank on recommendations for the design of haptic devices,
especially when they are taken from different experiments, with varying methods,
and when only a small number of participants took part”. The characteristic values
given here can only represent a selection of the analyses presented in literature. For
quite an actual and a very compelling summary [118] is recommended.
3.3 Conclusions from the Biology of Haptics
Next to studying the pure characteristic values of haptic perception we should keep
an eye on the real meaning of μm-elongations and frequencies of 1 kHz and more
and on its impact on real technical systems which is a small “finger exercise” for
you to be prepared for the challenges of the design of haptic systems; the idea of
this is based on a talk given by NIEMEYER at the Eurohaptics conference in 2006.
3.3.1 Stiffnesses
Already the initial touch of a material gives us information about its haptic properties.
A human is able to immediately discriminate, whether he or she is touching a
wooden table, a piece of rubber or a concrete wall with his or her finger tip. Besides
the acoustic and thermal properties, especially the tactile and kinaesthetic feedback
plays a large role. Based on the simplified assumption of a double-sided fixed plate
its stiffness k can be identified by the usage of the E-modulus according to equation
[158]
k = (2bh^3/l3)·E (3.8)
Figure 3.16a shows the calculation of stiffnesses for a plate of an edge length of
1 m and a thickness of 40 mm of different materials. In comparison, the stiffnesses
of commercially available haptic systems are given in (fig. 3.16b). It is obvious
that these stiffnesses of haptic devices are factors of ten lower than the stiffnesses
of concrete, every-day objects like tables and walls. However, stiffness is just one
criterion for the design of a good, haptic system and should not be overestimated.
The comparison above shall make us aware of the fact that a pure reproduction of
solid objects can hardly be realized with a single technical system. It rather takes a
combination of stiff and dynamic hardware, for especially the dynamic interaction
in high frequency areas dominates the quality of haptics, which has extensively been
discussed in the last section.
3.3.2 One Kilohertz - Significance for the Mechanical Design?
Haptic perception ranges to a frequency of 10 kHz, whereby the area of highest sensitivity
lies between 100 Hz and 1 kHz. This wide range of haptic perception enables
us to perceive microstructures on surfaces with the same accuracy as enabling us to
identify the point of impact when drumming with our fingers on a table. For a rough
calculation a model according to figure 3.17 is considered to be a parallel circuit
between a mass m and a spring k. Assuming an identical “virtual” volume V of material
and taking the individual density ρ for a qualitative comparison, the border
frequency for a step response can be calculated according to
fb=1/2*pi(sqrt(k/m))=1/2*pi(sqrt(k/vp))...(3.9)
Figure 3.17 shows the border frequencies of a selection of materials. Only in case
of rubber and soft plastics border frequencies of below 100 Hz appear. Harder plastic
material (Plexiglas) and all other materials show border frequencies above 700 Hz.
One obvious interpretation would state that any qualitatively good simulation of
such a collision demands at least such bandwidth of dynamics within the signal
conditioning elements and the mechanical system.
As a consequence, a frequent recommendation for the design of haptic systems
is the transmission of a full bandwidth of 1kHz (and in some sources even up to
10kHz). This requirement is valid with respect to software and communicationsengineering,
as sampling-systems and algorithmic can achieve such frequencies easily
today. Considering the mechanical part of the design, we see that dynamics of
1kHz are enormous, maybe even utopian. Figure 3.18 gives another rough calculation
of oscillating force amplitude according to
F0 = |x · (2π f )2m|. (3.10)
The basis of the analysis is a force source generating an output force F0. The
load of this system is a mass (e.g. a knob) of 10 grams (!!). The system does not
have any additional load, i.e.it does not have to generate any haptically active force
to a user. A periodic oscillation of a frequency f and an amplitude x is assumed.
With expected amplitudes for the oscillation of 1mm at 10 Hz a force of approximately
10mN is necessary. At a frequency of 100 Hz there is already a force of
2-3N needed. At a frequency of 700 Hz the force already increases to 100N - and
this is what happens when moving a mass of 10 grams. Of course in combination
with a user-impedance as load the amplitude of the oscillation will decrease in areas
of below 100μm, proportionally decreasing the necessary force. But this calculation
should make aware of the simple fact that the energetic design and power management
of electromechanical systems with application in the area of haptics needs to
be done very carefully.
The design of a technical haptic system is always a compromise between bandwidth,
stiffness, dynamics of signal conditioning and maximum force-amplitudes.
Even with simple systems the design process leads the engineer to the borders of
what is physically possible. Therefore it is necessary to have a good model for the
user according to his being a load to the mechanical system and according to his
or her haptic perception. This model enables the engineer to carry out an optimized
design of the technical system and its generation is the focus point of the following
chapter.
No comments:
Post a Comment