# Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study
Assembling a Human Hand Injury Protection Database

Robin Jeanne Kirschner1, Carina M. Micheler2,3, Yangcan Zhou1, Sebastian
Siegner1, Mazin Hamad1,
Claudio Glowalla2,4, Jan Neumann5, Nader Rajaei1, Rainer Burgkart2 and Sami
Haddadin1 1 Chair for Robotics and Systems Intelligence, Munich Institute of
Robotics and Machine Intelligence, Technical University of Munich, 80992
Munich, Germany
2 Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der
Isar, TUM School of Medicine, Technical University of Munich, 81675 Munich,
Germany
3 Institute for Machine Tools and Industrial Management, TUM School of
Engineering and Design, Technical University of Munich, 85748 Garching near
Munich, Germany
4 Endoprothetikzentrum der Maximalversorgung, BG Hospital Murnau, 82418
Murnau, Germany
5 Department of Diagnostic and Interventional Radiology, Klinikum rechts der
Isar, TUM School of Medicine, Technical University of Munich, 81675 Munich,
Germany
Corresponding author<EMAIL_ADDRESS>
###### Abstract

The use of pointed or edged tools or objects is one of the most challenging
aspects of today’s application of physical human-robot interaction (pHRI). One
reason for this is that the severity of harm caused by such edged or pointed
impactors is less well studied than for blunt impactors. Consequently, the
standards specify well-reasoned force and pressure thresholds for blunt
impactors and advise avoiding any edges and corners in contacts. Nevertheless,
pointed or edged impactor geometries cannot be completely ruled out in real
pHRI applications. For example, to allow edged or pointed tools such as
screwdrivers near human operators, the knowledge of injury severity needs to
be extended so that robot integrators can perform well-reasoned, time-
efficient risk assessments. In this paper, we provide the initial datasets on
injury prevention for the human hand based on drop tests with surrogates for
the human hand, namely pig claws and chicken drumsticks. We then demonstrate
the ease and efficiency of robot use using the dataset for contact on two
examples. Finally, our experiments provide a set of injuries that may also be
expected for human subjects under certain robot mass-velocity constellations
in collisions. To extend this work, testing on human samples and a
collaborative effort from research institutes worldwide is needed to create a
comprehensive human injury avoidance database for any pHRI scenario and thus
for safe pHRI applications including edged and pointed geometries.

##  I INTRODUCTION

Figure 1: Using the generalized hand injury protection database for a
constrained contact with a sharp edge can allow a reasonable risk assessment
for contacts with a Phillips head, provide permissible mass-velocity
combinations for safe impact scenarios, and allow deployment of safe velocity-
scaling based on the computable effective robot mass.

Safety in human-robot interaction is one of the major challenges that need to
be addressed for successful robot deployment [1, 2]. Especially with the trend
of flexible task execution in industrial assembly lines, collaborative human
robot application and contact-rich manipulation are becoming increasingly
important. Tasks that are being automated using robots are for instance,
(un)screwdriving [3, 4], collaborative manufacturing [5], or handling of
electronics parts [6]. Enabling, understanding and controlling contacts is a
fundamental prerequisite for this, which is made possible, e.g., by impedance
control/uniform impedance [7, 4].

Nevertheless, errors may still occur, e.g. slipping off the surface or
misplacing the tool or objects. In these cases proximity between human and
robot can result in hazardous scenarios like poking the hand with pointed tool
tips, cutting the skin with edges of grippers or objects, or even bone
injuries due to deep cuts with sheet objects. To enable such applications
nevertheless, requires an appropriate risk assessment and risk mitigation
measures [8]. The categorization of possible injury events according to their
severity is fundamental to this risk assessment. The currently available
standards provide a dataset for permissible contact forces and pressures based
on experimentally evaluated human onset of pain in blunt contacts [9]. The
compliance to these forces and pressures must be validated for the robotic
application. This means that impact forces and pressures must be measured in
an iterative process requiring costly test devices and additional test setups
[2, 10], which leads to long downtimes in the production line every time
parameters in the application are changed [11]. This makes the cost- and time-
efficient use of robots in unstructured or dynamically changing environments
more difficult and ultimately limits the potential for the next generation of
flexible automation.

TABLE I: Overview of studies reporting severity of harm for human hand available for risk assessment based on [8] Cat. |  Injury |  Studies | Subjects | Injury parameters | Impactor types
---|---|---|---|---|---
human | surrogate |  synthetic |  simulation |  $F$ |  $p$ |  $[m,v]$ |  $E$ |  edged |  blunt
iv |  ev |  iv |  ev |
S0 |  Onset of pain |  [9] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  ✓ |  - |  - |  - |  ✓
[12] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  ✓ |  ✓ |  ✓ |  - |  ✓
[13] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  ✓ |  - |  - |  - |  ✓
[14] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  - |  - |  - |  - |  ✓
[15] |  - |  - |  - |  - |  - |  ✓ |  ✓ |  ✓ |  (✓) |  ✓ |  - |  ✓
[16] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  - |  - |  - |  (✓) |  -
[17]111Data for human lower arm |  ✓ |  - |  - |  - |  - |  - |  ✓ |  - |  - |  ✓ |  (✓) |  ✓
|  |  [18] |  - |  ✓ |  - |  - |  - |  - |  ✓ |  - |  - |  - |  (✓) |  -
|  |  [19] |  ✓ |  - |  - |  - |  ✓ |  - |  ✓ |  - |  - |  - |  (✓) |  ✓
S1 |  skin contusion |  [20] |  ✓ |  - |  - |  - |  - |  - |  ✓ |  ✓ |  (✓) |  ✓ |  - |  ✓
S2 |  bone fracture |  [16] |  - |  ✓ |  - |  - |  - |  - |  ✓ |  - |  - |  - |  (✓) |  -
[21] |  - |  ✓ |  - |  - |  - |  - |  ✓ |  - |  - |  - |  (✓) |  -

Understanding the dynamics between human injury and collision scenarios in
physical human-robot interaction (pHRI) based on controllable and observable
parameters such as energy-based parameters like effective mass, velocity, and
contact curvature is a fundamental step to enable efficient and well reasoned
risk assessment [1, 2] as depicted in Fig. 1. This injury knowledge can best
be generated by human injury analysis which requires consideration of all
human body parts, and relevant medical factors such as age. Hence, a large
collection of datasets resulting from a multitude of experiments using
adequate surrogates or human tests is required [20, 1]. Historically, injury
testing was performed for deadly car crashes [22, 23] and severe clamping
situations [18]. Ensuring safety in pHRI, nevertheless, requires consideration
of injuries occurring in contact with lower effective masses and contact
velocities but varying contact geometries [1]. Especially, the human hand is
typically exposed to high injury potential in pHRI [15]. In order to create a
human injury protection database that can be efficiently used for risk
assessment, in this paper we first discuss the available datasets on human
hand injury protection in terms of the severity of harm suggested by industry
standards. Building on this, we propose studies to assess injury cases caused
by edged and pointed impact devices. To this end, we present two surrogates
for the analysis of human hand injuries based on anatomical similarities and
analyze their recurrent impact injury patterns. We then develop testbed,
procedures, and evaluation methods for a generalizable injury analysis based
on curvature, mass, and velocity of impact. Using 351 experiments with pig dew
claws and 117 experiments with chicken drumsticks as surrogates for the human
hand, we derive, to the best of the authors’ knowledge, the first database for
protecting the human hand from injury in edged and pointed impact geometries
using tetrahedral, wedge-shaped, and sheet-shaped impactors in constrained
contact scenarios. Finally, we demonstrate the application of the injury
protection database using the example of screwdriving and handling of
electronic components and discuss future developments. This paper is
structured as follows. Sec. II provides an overview of the state of the art in
human hand injury analysis. Then, Sec. III motivates and describes the
conducted injury experiments and validation study. Following Sec. IV and Sec.
V list and discuss the results and Sec. VI concludes the paper.

##  II Available Human Hand Injury Data

Enabling risk assessment requires estimation of the severity of harm resulting
in a impact [8]. Consequently, for pHRI deployment, we require datasets
relating human injury occurrence to robot parameters [1]. For this, three
categories of fundamental information are required to relate the available
injury datasets to the concrete automation application at hand:

  * a)

Subject type (including body location),

  * b)

Measured injury-inducing parameters, and

  * c)

Considered impactor geometry.

Subjects for injury experiments are typically in-vivo (iv) or ex-vivo (ev)
human specimen [12] or animal surrogates [1], synthetic specimen like dummies
[24], or even simulations [15]. General injury parameters reported in
literature are forces $F$ [9], pressure $p$ [9], mass and velocity pairs
$[m,v]$ [1], and energy-based metrics $E$ such as impact energy density[17],
total transferred energy [25], or impulse[18]. Lastly, the impactor geometries
that encodes the impact curvature are variable, but can generally be
distinguished into blunt or edged impactor geometries. Here, we classify boned
but not rounded impactors as edged [17, 1] and flat, rounded impactors with
contact surfaces of $\approx$ $1\text{\,}\mathrm{c}\mathrm{m}^{2}$ as blunt
[12]. Table I lists available human hand injury biomechanics datasets to the
best of the authors’ knowledge, in line with our previous literature reviews
[26, 27]222(✓) marks impactors with $A<$ $1\text{\,}\mathrm{c}\mathrm{m}^{2}$
but rounded edges for _edged_ and incomplete data for _[m,v]_..

It is evident that especially impacts with edged impactors are not well
researched for adequate risk assessments, although fully avoiding edged
contacts does not seem feasible considering pHRI applications in industrial
scenarios. Moreover, the directly controllable/computable robot parameters
(namely velocity and effective mass) that cause injuries are hardly reported,
which complicates the integration process into robot control. This is also
reflected by the state of the art standardization, which provides force and
pressure thresholds based on contact geometries $\geq$
$1\text{\,}\mathrm{c}\mathrm{m}^{2}$ and demands force and pressure analysis
in contact by experimental risk analysis [28]. To systematically address this
gap and enable efficient risk assessment for any robotic application
(including edged contact geometries), we derive an initial datasets for ex-
vivo human hand surrogates.

##  III Methodology

This section describes the test conditions, the evaluation procedures and the
test setups used to derive human injury protection datasets. It also describes
the validation experiment to demonstrate the application of the generated
injury protection datasets for risk reduction.

Figure 2: MRI scan and cross-section of the different experiment specimen as
compared to the human hand [29], key tissue types indicated for different
specimen.

###  III-A Experimental Subject Selection

This study focuses on hand injuries, as these are the parts of the body that
interact most with collaborative robots. Various types of surrogates are
suitable as experimental subjects. Synthetic models are ethically
unobjectionable. However, their ability to replicate biomechanical properties
is limited. Animal and cadaver specimens come closer to the biomechanical
conditions of living humans and are preferred in this experimental setup. In
the literature, porcine specimens are the most commonly used animal specimens
[30, 31, 32, 33, 25, 1], as they approximate the biomechanical properties of
the human. The front feet of a pig with the corresponding soft tissues can be
used to mimic the human hand, and the pig toes mimic the general structure of
human fingers well, as shown in Fig. 2 is shown. The metacarpal and proximal
phalanx of the pig claw best resemble human fingers, as they are thinner than
their counterparts. One limitation is that the skin of pigs is thermally
treated during slaughter, which changes its structural properties and makes
the skin tougher. On this basis, chicken thighs (Gallus domesticus) are chosen
as a second surrogate to provide a reference in particular for the range of
skin lesions that can occur in real human specimens, although the chicken’s
anatomical similarity to humans is limited, especially due to the bird’s
hollow bones. The average thickness of chicken skin (epidermis + dermis) can
range from $0.6\text{\,}$ to $1.5\text{\,}\mathrm{m}\mathrm{m}$, depending on
age, feeding and skin area [34, 35]. In contrast, the thickness of porcine
skin varies between $1\text{\,}$ and $6\text{\,}\mathrm{m}\mathrm{m}$ [31,
36]. The human skin (epidermis + dermis) on the back of the hand in comparison
is about $2.3\text{\,}\mathrm{m}\mathrm{m}$ thick [37].

In this study, we use porcine specimens (dew claws), as previously applied in
the above listed literature and compare them with chicken drumsticks.

Figure 3: Observable features representing injury types occurring upon
impacting the pig dew claw and chicken drumstick.

###  III-B Injury Evaluation

Classification patterns for fractures associated with soft tissue injuries
include the so-called AO classification established by the Association of the
Study of Internal Fixation [38], the Gustilo-Anderson classification [39] and
the Oestern and Tscherne classification [40]. Based on these classifications,
in a preliminary study, we noted the observable injuries made in impacts with
the test specimens which were labelled by a medical professional as exemplary
depicted in Fig. 3. For the pig dew claws we observed skin imprints, skin
injuries (including cuts and lacerations), muscle injuries, and bone injuries
(including visible imprints and fractures) as depicted on the top of Fig. 3.
For the chicken drumsticks we also observed skin imprints, skin cuts, muscle
injuries, and bone injuries as presented on the bottom of Fig. 3. For visual
analysis of the skin injuries we first analyse the specimen epidermis
superficially while for the bone and fibre injuries we open the specimen to
the layer of interest. The different injuries types are further referred to
using the presented marker in Fig. 3.

###  III-C Impact Experiments

####  III-C1 Experimental Setup Design

TABLE II: Experimental settings Parameter |  |  Value |  Motivated by
---|---|---|---
effective mass |  |  $0.5\text{\,}$-$8\text{\,}\mathrm{k}\mathrm{g}$ |  [41]
velocities |  |  $0.20\text{\,}$-$2.0\text{\,}\mathrm{m}\mathrm{/}\mathrm{s}$ |  [28], [42]
impact geometries |  wedge |  prism $None$, boned |  [1]
edge |  tetrahedron $None$, boned |  [1]
sheet |  width $1.5\text{\,}\mathrm{m}\mathrm{m}$, boned |  [6]
Figure 4: Impact drop testing and subject placement: Drop test device (i),
mass unit with impactor and specimen (ii) and the applied impactors and
prepared specimen, pig (P) and chicken (C) with three distinct impact
locations (1-3 from distal to proximal).

Drop testing is a common procedure for material testing, which is lately also
applied for injury analysis in sports [24] and robotics [1], motivating the
design of our test bed. The developed drop test device is depicted in Fig. 4
(i). Its control and data acquisition runs via a compactRio system from
company National Instruments and samples all data at
$2000\text{\,}\mathrm{H}\mathrm{z}$. The linear guided sledge is lifted up by
electromagnets attached to a carrier system on linear units as presented in
Fig. 4. On the sledge, a falling mass unit is integrated (c.f. Fig. 4 (ii))
that yields desired masses ranging from $0.6\text{\,}\mathrm{k}\mathrm{g}$ up
to maximum $40\text{\,}\mathrm{k}\mathrm{g}$. The additional mass is added on
the mass unit as depicted in Fig. 4 a). At the end of the falling mass unit
the impactor is mounted, see Fig. 4 c). In this study, we chose the following
impactors (check Table II for details)

  * •

Wedge (W); Fig. 4 1),

  * •

Edge (E); Fig. 4 2), and

  * •

Sheet (S); Fig. 4 3).

These impactors are made from aluminium alloy EN AW-7075 with hardness
$150\text{\,}\mathrm{H}\mathrm{B}$. The impact velocity is obtained by a
calibration curve relating the relative height to the impact velocity measured
using a DAkkS-calibrated precision light barrier (Precision lightbarrier
203.10 from company Hentschel [43]). The calibration is obtained by a
measurement routine where the light barrier is positioned at the point of
contact and a calibration object crosses the laser beam right before collision
from different relative drop heights. An uni-axial piezo-electric force sensor
(Type 9331C from Kistler [44]), Fig. 4 b), with amplifier LabAmp 5165A [45]
running at $10\text{\,}\mathrm{k}\mathrm{H}\mathrm{z}$ is integrated to
measure the contact force resulting on the probes under the impactor, see Fig.
4 d). For safe placing of the probes under the drop test by the operator, a
table with linear unit, that can be moved out of the testing area, was
integrated.

####  III-C2 Experimental Protocol

The overall experimental protocol is depicted in Fig. 5. In the following, we
detail the experimental steps.

Figure 5: Experimental Flowchart.

Preparation procedure: As summarized in Fig. 5 a) and b), the preparation
procedure first requires the frozen samples used on a test day ($\approx$ 4
pig feet or 8 chicken legs) to be thawed at room temperature for approx. 10
hours. The samples are then prepared by dissecting the individual pig dew
claws and chicken legs. The specimen are examined for existing injuries, which
are marked, and finally the specimen appearance is documented
photographically. Then, the impact locations are indicated by skin markers,
excluding the dew claw joints for the pork specimen. Then, the desired
dropping height based on the desired velocity and impact mass are adjusted.
Additionally, the positioning of the specimen is adjusted, such that the
desired impact location is reached. Currently unused samples were stored with
moisture to prevent structural skin changes.

Experimental Phase: The drop test device is used to cause the impacts
following the procedure depicted in Fig. 5 c) and d). First, the device is
started. Then, the carrier moves in contact to the sledge and pulls the the
sledge up to the defined height. To load the specimen, the table moves out.
The specimen placement is adjusted by the operator. Afterwards, the operator
triggers the test from the PC and the table automatically moves back to its
initial position. After the table moved in place, all measurement devices are
activated. Lastly, the magnets release the sledge, which falls and is then
caught by a damper unit. Consequently, only the detached mass unit strikes the
sample at the desired location and at the speed obtained from the drop height.
This process is repeated at all three defined impact locations of each sample
and with three freshly prepared samples. Finally, the procedure is repeated
with the next mass-velocity configuration.

Postprocessing: Once the device turned off, the examiner fetches the probe for
a visual analysis of the skin. Then, the specimen is opened and bone or tissue
injuries are observed. All findings are documented photographically and video
recordings are stored for later reference. The drop testing procedure causes
consecutively a dynamic (transient) collision force of short duration
($<$$0.5\text{\,}\mathrm{s}$) followed by the impactor resting on the specimen
as quasi-static load [9]. As the resting load from our dropping weight causes
quasi-static forces of $\sim$ $6\text{\,}\mathrm{N}$ \-
$36\text{\,}\mathrm{N}$, while the impacting forces are six to 30-times
higher, we assume the observed injuries to result from the initial, dynamic
impact phase.

###  III-D Validation study

Figure 6: Validation scenarios screw driving and breadboard handling with
constrained contact between the surrogate for the human hand and the Phillips
head/bread board to the wall.

To substantiate the applicability of the established database for safe robot
deployment, we show the according risk reduction procedure based on our
generated datasets for edged and pointed impactors using a screw driving [4]
and electronics (breadboard) handling [6] application. The Phillips head of
the screwdriver, can conservatively be simplified with our edged impactor
geometry333Note that the _chamfered_ edges of the Phillips head are expected
to cause less shear than the only _slightly boned_ edges of the pointed
impactor E.. The breadboard is of same thickness the sheet impactor and both
are only boned. To enable efficient tool-usage with such geometries, the first
requirement is that the effective mass at the constrained impact location
should be as low as possible. For this, we obtain the robot joint
configurations allowing low effective masses at the point of interest
$m_{\mathrm{POI}}$ by simulation following the effective mass evaluation in
[46, 47, 48]. The robot configuration
$q=[0.013,-0.305,-0.069,-1.90,-0.02,1.59,0.78]^{\mathsf{T}}$ was found ideal
to conduct both example tasks and provide a low effective mass of
$m_{\mathrm{POI}}=$ $1.50\text{\,}\mathrm{k}\mathrm{g}$ and
$m_{\mathrm{POI}}=$ $1.92\text{\,}\mathrm{k}\mathrm{g}$ during motion in
$x$-axis direction of the base frame with the Cartesian unit direction being
$\boldsymbol{u}=[1\,0\,0]$ for the screw driver and breadboard setup,
respectively. Based on the previously established hand injury protection
database, one can approximate the maximum permissible velocities by, e.g.,
considering skin imprints as only acceptable observations.

As a validation for each setup pig dew claws are used as human finger
surrogates as depicted in Fig. 6. The robot was programmed such that a
relative Cartesian motion of $30\text{\,}\mathrm{c}\mathrm{m}$ is performed by
moving the end-effector towards the wooden wall at safe velocity according to
the previously generated datasets. During motion the tip of the screwdriver or
edge of the breadboard respectively collides with the pig specimen
approximately after $15\text{\,}\mathrm{c}\mathrm{m}$. Directly after contact
the skin injury is examined and documented. Then, the impact location is
changed aiming for at least six contact points444Joint spaces and regions of
visible previous injury were excluded.. This procedure is conducted for three
different pig dew claws, at the proximal phalanx and metacarpal of the dew
claw. After all impacts were successfully established, the specimen is
dismounted from the test location and the superficial skin (epidermis)
injuries are examined visually. Then, the specimen is placed in the fridge for
an hour and remaining skin injuries are reported. Lastly, deeper skin
(dermis), muscle, and bone injuries were examined by dissection.

Figure 7: Injury datasets obtained with a) wedge impactor, b) edge impact, and
c) sheet impactor for pig dew claws and chicken drumsticks on the three
different impact locations.

##  IV Results

In this study, a total of 117 pig dew claws and 39 chicken drumsticks were
tested in 351 and 117 impact experiments. In the following, the results are
presented descriptively. First, all obtained injury protection datasets are
listed for the two specimen types, as well as for the different impact
locations and impactors. Then, injury probabilities and impact forces
corresponding to the injury types are listed for the different impactors and
specimens. Finally, validation results are presented. Please note that we do
not perform a statistical analysis, as the variety of test conditions and the
small number of repetitions under similar conditions ($n=3$) do not allow any
statistical conclusions to be drawn.

###  IV-A Injury experiments

Fig. 7 depicts the three obtained injury prevention datasets for pig dew claw
and chicken drumsticks as surrogates for human hands using impactor W, E, and
S, subdivided into three impact locations. For the pig dew claw specimens,
mainly skin imprints and cuts can be observed for all impactors. In case of
impact with the impactor S, bone injuries occurred with velocities higher than
$1.0\text{\,}\mathrm{m}\mathrm{/}\mathrm{s}$ when the mass was
$2.6\text{\,}\mathrm{k}\mathrm{g}$. Impactor E caused additionally muscle
injury in case of velocities higher than
$1.0\text{\,}\mathrm{m}\mathrm{/}\mathrm{s}$ when the mass was $\geq$
$0.6\text{\,}\mathrm{k}\mathrm{g}$. For the chicken specimen, we observed
mainly muscle injuries and bone fractures. Only in the distal impact location
on the drumstick skin imprints or cuts occurred. The probability of certain
observations for all impact locations clustered by muscle and bone injuries
(m/b) and skin cuts, muscle and bone injuries (s/m/b) is provided in Fig. 8
for the pig specimens.

Figure 8: Overview of the probability of injuries of pig dew claws grouped by
injury types occurring resulting the drop testing.

For descriptive analysis, we link the measured force data to the injury type
that occurred during the experiments as depicted in Fig. 9. For the pig dew
claws, skin imprints (0) were observed at
$498\text{\,}$$\pm$$389\text{\,}\mathrm{N}$, skin cuts (1) at
$1279\text{\,}$$\pm$$772\text{\,}\mathrm{N}$, and no other observations were
made using the impactor W. The caused skin imprints with forces of
$197\text{\,}$$\pm$$192\text{\,}\mathrm{N}$, skin cuts (2) occurred with
$496\text{\,}$$\pm$$391\text{\,}\mathrm{N}$, and bone injuries were registered
at $796\text{\,}$$\pm$$531\text{\,}\mathrm{N}$. For impactor S, skin imprints
occurred at $350\text{\,}$$\pm$$351\text{\,}\mathrm{N}$,
$860\text{\,}$$\pm$$448\text{\,}\mathrm{N}$ caused skin cuts, and bone
injuries occurred around $1469\text{\,}$$\pm$$346\text{\,}\mathrm{N}$. The
chicken drumsticks showed skin imprints at
$77\text{\,}$$\pm$$20\text{\,}\mathrm{N}$, skin cuts at
$93\text{\,}$$\pm$$27\text{\,}\mathrm{N}$, muscle injury at
$82\text{\,}$$\pm$$51\text{\,}\mathrm{N}$, and bone injuries at
$137\text{\,}$$\pm$$39\text{\,}\mathrm{N}$ for W, For the impactor E, skin
imprints occurred at $32\text{\,}$$\pm$$8\text{\,}\mathrm{N}$, the skin was
cut at $78\text{\,}$$\pm$$18\text{\,}\mathrm{N}$, muscle injuries showed at
$51\text{\,}$$\pm$$28\text{\,}\mathrm{N}$, and bone injuries at
$95\text{\,}$$\pm$$22\text{\,}\mathrm{N}$ for impactor E. In case of the
impactor S, the forces were $19\text{\,}$$\pm$$3\text{\,}\mathrm{N}$,
$94\text{\,}$$\pm$$0\text{\,}\mathrm{N}$,
$98\text{\,}$$\pm$$64\text{\,}\mathrm{N}$, and
$168\text{\,}$$\pm$$54\text{\,}\mathrm{N}$ for the respective observations.

Figure 9: Overview of reported forces that caused certain medically observed
injuries.

###  IV-B Validation result

TABLE III: Results of the validation studies Phillips head | Breadboard
---|---
$v_{\mathrm{r}}$ [m/s] |  $\Sigma_{\mathrm{t}}$ |  $\Sigma_{\mathrm{s}}$ |  $i$ |  s/m/b |  $v_{\mathrm{r}}$ [m/s] |  $\Sigma_{\mathrm{t}}$ |  $\Sigma_{\mathrm{s}}$ |  $i$ |  s/m/b
0.1 |  19 |  16 |  8 |  0 |  0.2 |  13 |  13 |  13 |  0

Table III reports the applied maximum velocity $v_{\mathrm{r}}$ number of
total trials conducted when intending to hit the tied-up pig dew claws on the
bone $\Sigma_{\mathrm{t}}$, the amount of successful contacts with the bone
$\Sigma_{\mathrm{s}}$, the amount of observed skin imprints $i$, and the
amount of skin, muscle, or bone injury (s/m/b) observations.

##  V DISCUSSION

We conclude from the generalizable drop test experiments on surrogate specimen
and their validation study that by velocity-shaping based on the hand injury
protection datasets even in constrained impact scenarios with edged and
pointed objects, it can be ensured that collision scenarios with the human
hand do not result in open skin, muscle or bone injuries. Impacts with edged
or pointed geometries that do not cause such injuries seem feasible with
contact velocity $\leq$$0.5\text{\,}\mathrm{m}\mathrm{/}\mathrm{s}$, but also
with higher velocities up to $1.0\text{\,}\mathrm{m}\mathrm{/}\mathrm{s}$
depending on the effective robot and payload mass. Thus, of great importance
for safely handling pointed or edged objects becomes the robot motion and
workspace design considering the effective mass of the system at possible
clamping positions. For unconstrained contact situations, the presented
datasets can be applied as conservative approximation. Additional research
should, nevertheless, consider injury caused by unconstrained contacts as well
as shearing contacts.Overall, we observe strong differences between the two
ex-vivo animal surrogates. Since the bone structure of chickens is hollow in
contrast to human hand bones, we observed many scenarios that resulted in bone
fractures in the chicken specimen. The pig dew claw bone seems feasible as a
human finger bone surrogate based on the observed bone injuries upon contact
with the impactor S of the pig claw
($\approx$$1000\text{\,}$-$1800\text{\,}\mathrm{N}$, see Fig. 9) compared to
Hohendorff et al. [16], who measured the onset of bone fractures in ex-vivo
human fingers clamped in car windows with mostly $1011\text{\,}$\-
$2129\text{\,}\mathrm{N}$ with outliers up to $291\text{\,}\mathrm{N}$.

Finally, only ex-vivo human testing in the considerable parameter space can
validate the use of surrogates and reveal the expectable human injury
observations. While ex-vivo specimen experiments allow observation of
potential macroscopic skin injuries and similar they do not allow the
unambiguous observation of skin contusions (bruises). Only the comparison with
in-vivo specimen will allow to differentiate between occurrence of contusions
or no injuries after all, e.g., rabbit legs [49]. However, the required number
of such studies should be minimized as much as possible by prior ex-vivo
observations.

##  VI CONCLUSION

In this work, we propose to create a human hand injury protection database to
enable efficient risk assessment and safe integration of pointed or edged
tools or objects in pHRI. We start building the database by replacing
experiments with generalizable impact studies using a developed drop test
device. In this study, we analyze the occurrence of injuries in 351 impacts
with pig dew claws and 117 experiments with chicken drumsticks hit by boned
tetrahedrons, prisms, and sheet-shaped geometries. In two validation
scenarios, we use the obtained datasets to demonstrate their application for
safe pHRI design. As a result, we observe that even for constrained impacts
with edged or pointed geometries, impacts scenarios can be designed such that
no open skin, muscle or bone injuries occur if the effective masses and
velocities near pinch points are closely taken into account. This finding
opens the door for further investigations and promises to lead to efficient
safety implementations for pHRI applications in practice, where edges and
corners can never be completely avoided. With a collaborative effort of
medical and robotics researchers around the world, we can finally create a
complete database of human injuries that takes into account multiple settings
and application areas for safe pHRI.

## ACKNOWLEDGMENT

The authors would like to thank Bach Tran for supporting the test stand
design. We gratefully acknowledge the funding of the Lighthouse Initiative
KI.FABRIK Bayern by StMWi Bayern, Forschungs- und Entwicklungsprojekt, grant
no. DIK0249, the European Union’s Horizon 2020 research and innovation
programme as part of the project ReconCycle under grant no. 871352, and of the
Bavarian State Ministry for Economic Affairs, Regional Development and Energy
(StMWi) as part of the project SafeRoBAY (grant number: DIK0203/01).

## References

  * [1] S. Haddadin, S. Haddadin, A. Khoury, T. Rokahr, S. Parusel, R. Burgkart, A. Bicchi, and A. Albu-Schäffer, “On making robots understand safety: Embedding injury knowledge into control,” _The International Journal of Robotics Research_ , vol. 31, no. 13, pp. 1578–1602, 2012.
  * [2] J. Lachner, F. Allmendinger, E. Hobert, N. Hogan, and S. Stramigioli, “Energy budgets for coordinate invariant robot control in physical human–robot interaction,” _The International Journal of Robotics Research_ , vol. 40, no. 8-9, pp. 968–985, 2021.
  * [3] N. Villa, E. Mobedi, and A. Ajoudani, “A contact-adaptive control framework for co-manipulation tasks with application to collaborative screwing,” in _2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)_ , 2022, pp. 1131–1137.
  * [4] S. Hjorth, E. Lamon, D. Chrysostomou, and A. Ajoudani, “Design of an energy-aware cartesian impedance controller for collaborative disassembly,” _arXiv preprint arXiv:2302.03587_ , 2023.
  * [5] F. Tassi, F. Iodice, E. De Momi, and A. Ajoudani, “Sociable and ergonomic human-robot collaboration through action recognition and augmented hierarchical quadratic programming,” in _2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_ , 2022, pp. 10 712–10 719.
  * [6] A. Casalino, F. Cividini, A. M. Zanchettin, L. Piroddi, and P. Rocco, “Human-robot collaborative assembly: a use-case application,” _IFAC-PapersOnLine_ , vol. 51, no. 11, pp. 194–199, 2018, 16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896318313818
  * [7] K. Karacan, D. Grover, H. Sadeghian, F. Wu, and S. Haddadin, “Tactile exploration using unified force-impedance control,” in _22nd IFAC World Congress_ , 2023.
  * [8] DIN EN ISO 12100:2011-03, Safety of machinery - General principles for design \- Risk assessment and risk reduction (ISO 12100:2010); German version EN ISO 12100:2010.
  * [9] DIN ISO/TS 15066:2016-02, Robots and robotic devices –- Collaborative robots (ISO/TS 15066:2016).
  * [10] C. Fischer, M. Steiner, M. Neuhold, M. Papa, A. Markis, and S. Schlund, “An investigation of the measurement of transient contacts in human-robot interaction,” in _International Conference on Robotics in Alpe-Adria Danube Region_. Springer, 2022, pp. 547–555.
  * [11] R. J. Kirschner, N. Mansfeld, S. Abdolshah, and S. Haddadin, “Iso/ts 15066: How different interpretations affect risk assessment,” 2022.
  * [12] R. Behrens and N. Elkmann, “Study on meaningful and verified thresholds for minimizing the consequences of human-robot collisions,” in _2014 IEEE International Conference on Robotics and Automation (ICRA)_ , 2014, pp. 3378–3383.
  * [13] R. Behrens , G. Pliske , M. Umbreit , S. Piatek , F. Walcher , and N. Elkmann , “A statistical model to determine biomechanical limits for physically safe interactions with collaborative robots,” _Frontiers in Robotics and AI_ , vol. 8, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/frobt.2021.667818
  * [14] Y. Yamada, Y. Hirasawa, S. Huang, and Y. Umetani, “Fail-safe human/robot contact in the safety space,” in _Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN’96 TSUKUBA_ , 1996, pp. 59–64.
  * [15] U. Asad, S. Rasheed, W. A. Lughmani, T. Kazim, A. Khalid, and J. Pannek, “Biomechanical modeling of human-robot accident scenarios: A computational assessment for heavy-payload-capacity robots,” _Applied Sciences_ , vol. 13, no. 3, 2023.
  * [16] B. Hohendorff, C. Weidermann, P. Pollinger, K. J. Burkhart, and L. P. Müller, “Jamming of fingers: An experimental study to determine force and deflection in participants and human cadaver specimens for development of a new bionic test device for validation of power-operated motor vehicle side door windows,” _Biomedizinische Technik_ , vol. 58, no. 1, p. 39 – 49, 2013\.
  * [17] B. Povse, D. Koritnik, T. Bajd, and M. Munih, “Correlation between impact-energy density and pain intensity during robot-man collision,” in _2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics_, 2010, pp. 179–183.
  * [18] B. Hohendorff, C. Weidermann, P. Pollinger, K. Burkhart, M. Konerding, K. Prommersberger, and P. Rommens, “Entrapment of adult fingers between window glass and seal entry of a motor vehicle side door: An experimental study for investigation of the force at the subjective pain threshold,” _Journal of Biomechanics_ , vol. 44, no. 11, p. 2158 – 2161, 2011.
  * [19] D. Mewes and F. Mauser, “Safeguarding crushing points by limitation of forces,” _International Journal of Occupational Safety and Ergonomics_ , vol. 9, no. 2, pp. 177–191, 2003, pMID: 12820907. [Online]. Available: https://doi.org/10.1080/10803548.2003.11076562
  * [20] R. Behrens, G. Pliske, S. Piatek, F. Walcher, and N. Elkmann, “A statistical model to predict the occurrence of blunt impact injuries on the human hand-arm system,” 2023.
  * [21] R. Kent, S. Stacey, and C. Parenteau, “Dynamic pinch tolerance of the phalanges and interphalangeal joints,” _Traffic injury prevention_ , vol. 9, no. 1, pp. 83–88, 2008.
  * [22] A. Ndiaye, M. Chambost, and M. Chiron, “The fatal injuries of car drivers,” _Forensic Science International_ , vol. 184, no. 1, pp. 21–27, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0379073808004581
  * [23] A. L. Haynes and H. R. Lissner, “Experimental head impact studies,” _Proceedings: American Association for Automotive Medicine Annual Conference_ , vol. 5, pp. 158–170, 1961. [Online]. Available: http://dx.doi.org/
  * [24] D. Haid, O. Duncan, J. Hart, and L. Foster, “Free-fall drop test with interchangeable surfaces to recreate concussive ice hockey head impacts,” _Sports Engineering_ , vol. 26, no. 1, pp. 1–11, 2023.
  * [25] R. Sugiura, T. Fujikawa, R. Nishikata, and T. Nishimoto, “Soft tissue bruise injury by blunt impact in human-robot interaction - difference of tolerance between chest and extremities,” in _2019 19th International Conference on Control, Automation and Systems (ICCAS)_ , 2019, pp. 792–797.
  * [26] M. Hamad, A. Kurdas, S. Abdolshah, and S. Haddadin, “A robotics perspective on experimental injury biomechanics of human body upper extremities,” in _2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR)_ , 2021, pp. 316–320.
  * [27] ——, “Experimental injury biomechanics of human body upper extremities: Anatomy, injury severity classification, and impact testing setups,” in _2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR)_ , 2021, pp. 310–315.
  * [28] E DIN EN ISO 10218-1:2021-09, Robots and robotic devices - Safety requirements for industrial robots - Part 1: Robot systems and integration (prEN ISO 10218-1:2021).
  * [29] A. Micheau and D. Hoa, “Die Hand (MRT): Atlas der Anatomie - medizinische Bildgebung,” June 2015. [Online]. Available: https://doi.org/10.37019%2Fe-anatomy%2F83480.de
  * [30] J. Ankersen, A. E. Birkbeck, R. D. Thomson, and P. Vanezis, “Puncture resistance and tensile strength of skin simulants,” _Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine_ , vol. 213, no. 6, pp. 493–501, 1999.
  * [31] O. A. Shergold, N. A. Fleck, and D. Radford, “The uniaxial stress versus strain response of pig skin and silicone rubber at low and high strain rates,” _International Journal of Impact Engineering_ , vol. 32, no. 9, pp. 1384–1402, 2006.
  * [32] N. M. Germscheid, G. M. Thornton, D. A. Hart, and K. A. Hildebrand, “A biomechanical assessment to evaluate breed differences in normal porcine medial collateral ligaments,” _Journal of Biomechanics_ , vol. 44, no. 4, pp. 725–731, 2011.
  * [33] J. N. Marchant-Forde and M. S. Herskin, _Pigs as laboratory animals_. Elsevier Ltd, 2018, no. 2011\. [Online]. Available: http://dx.doi.org/10.1016/B978-0-08-101012-9.00015-0
  * [34] C. H. Oliveira, K. M. Dias, R. D. Bernardes, T. F. Diana, R. J. Rodrigueiro, A. A. Calderano, and L. F. Albino, “The effects of arginine supplementation through different ratios of arginine:lysine on performance, skin quality and creatine levels of broiler chickens fed diets reduced in protein content,” _Poultry Science_ , vol. 101, no. 11, pp. 1–9, 2022.
  * [35] S. Bharathi, V. Indu, K. Lucy, S. Maya, A. Sreeranjini, V. Vasudevan, and C. Sunanda, “Histological studies on the oesophageal tonsils of broiler ducks,” _Journal of Food and Animal Sciences_ , vol. 1, no. 1, pp. 53–56, oct 2020. [Online]. Available: http://jfas.vetinfo.in/articles/histological-studies-on-the-oesophageal-tonsils-of-broiler-ducks/
  * [36] A. Summerfield, F. Meurens, and M. E. Ricklin, “The immunology of the porcine skin and its value as a model for human skin,” _Molecular Immunology_ , vol. 66, no. 1, pp. 14–21, 2015.
  * [37] P. Oltulu, B. Ince, N. Kökbudak, S. Findik, and F. Kiliç, “Measurement of epidermis, dermis, and total skin thicknesses from six different body regions with a new ethical histometric technique,” _Turkish Journal of Plastic Surgery_ , vol. 26, no. 2, pp. 56–61, 2018.
  * [38] T. P. Rüedi, W. M. Murphy, C. L. Colton, G. E. Fackelman, and Y. Harder, _AO principles of fracture management_. Thieme Stuttgart, 2000.
  * [39] R. Gustilo and J. Anderson, “Prevention of infection in the treatment of one thousand and twenty-five open fractures of long bones: retrospective and prospective analyses,” _The Journal of bone and joint surgery. American volume_ , vol. 58, no. 4, p. 453—458, June 1976. [Online]. Available: http://europepmc.org/abstract/MED/773941
  * [40] H.-J. Oestern and H. Tscherne, “Pathophysiology and Classification of Soft Tissue Injuries Associated with Fractures,” _Fractures with Soft Tissue Injuries_ , no. 3, pp. 1–9, 1984.
  * [41] R. J. Kirschner, N. Mansfeld, G. G. Peña, S. Abdolshah, and S. Haddadin, “Notion on the correct use of the robot effective mass in the safety context and comments on iso/ts 15066,” in _2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR)_ , 2021, pp. 6–9.
  * [42] Franka Emika GmbH, “FRANKA RESEARCH 3 - Datasheet,” Tech. Rep., 2022.
  * [43] Hentschel System GmbH, “Präzisions-Messlichtschranke 203.10 für Messdistanzen bis 60mm,” https://hentschel-system.de/de/produkte/geschwindigkeit/praezisions-messlichtschranke-20310, Last Accessed: 08/21/23.
  * [44] Kistler Group, “Piezoelectric Load Cells,” https://kistler.cdn.celum.cloud/SAPCommerce_Download_original/003-556e.pdf, Last Accessed: 08/21/23.
  * [45] ——, “Laboratory charge amplifiers LabAmp with data acquisition, for dynamic and or quasi-static signals, up to 8 channels / LabAmp,” https://www.kistler.com/DE/de/cp/labor-ladungsverstaerker-labamp-mit-datenerfassung-labamp/P0000289, Last Accessed: 08/21/23.
  * [46] M. Hamad, A. Kurdas, N. Mansfeld, S. Abdolshah, and S. Haddadin, “Modularize-and-conquer: A generalized impact dynamics and safe precollision control framework for floating-base tree-like robots,” _IEEE Transactions on Robotics_ , vol. 39, no. 4, pp. 3200–3221, 2023.
  * [47] M. Hamad, N. Mansfeld, S. Abdolshah, and S. Haddadin, “The role of robot payload in the safety map framework,” in _2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_ , 2019, pp. 195–200.
  * [48] O. Khatib, “Inertial properties in robotic manipulation: An object-level framework,” _The international journal of robotics research_ , vol. 14, no. 1, pp. 19–36, 1995.
  * [49] T. Fujikawa, K. Okai, Y. Yamada, N. Rajaei, and T. Nishimoto, “Development of in-vivo rabbit model of human finger skin injuries for determining safety criteria for human-robot contact,” in _2023 23rd International Conference on Control, Automation and Systems (ICCAS)_ , 2023, pp. 1146–1151.