# Monitoring the Mental State of Cooperativeness for Guiding an Elderly Person
in Sit-to-Stand Assistance

John Bell and H. Harry Asada John Bell and H. Harry Asada are with the
Department of Mechanical Engineering, Massachusetts Institute of Technology,
77 Massachusetts Ave., Cambridge MA 02139, USA jhbell<EMAIL_ADDRESS>
###### Abstract

In providing physical assistance to elderly people, ensuring cooperative
behavior from the elderly persons is a critical requirement. In sit-to-stand
assistance, for example, an older adult must lean forward, so that the body
mass can shift towards the feet before a caregiver starts lifting the body. An
experienced caregiver guides the older adult through verbal communications and
physical interactions, so that the older adult may be cooperative throughout
the process. This guidance is of paramount importance and is a major challenge
in introducing a robotic aid to the eldercare environment.

The wide-scope goal of the current work is to develop an intelligent eldercare
robot that can a) monitor the mental state of an older adult, and b) guide the
older adult through an assisting procedure so that he/she can be cooperative
in being assisted. The current work presents a basic modeling framework for
describing a human’s physical behaviors reflecting an internal mental state,
and an algorithm for estimating the mental state through interactive
observations. The sit-to-stand assistance problem is considered for the
initial study. A simple Kalman Filter is constructed for estimating the level
of cooperativeness in response to applied cues, with a thresholding scheme
being used to make judgments on the cooperativeness state.

##  I Introduction

According to a report [1], over 80% of those who died from COVID-19 in the
United States were age 65 or older, and over 40% of those individuals were
either nursing home residents or staff. Additionally, as of April 28, 2021, it
is estimated that over one million people in the US have been infected at
nursing homes and long-term care facilities [2]. High COVID-19 transmission
rates at nursing homes can likely be attributed to cluster infections caused
by current in-person eldercare practices. The overarching goal of the current
work is to develop a robotic aid for assisting older adults with virtually no
direct physical interactions between an older adult and a caregiver.

Existing elderly support technology has served primarily for reducing
caregiver physical effort and making maneuvers more efficient. One common
example are ceiling lifts and floor lifters, such as the Arjo Maxi Sky [3],
which are used to fully support the weight of elderly patients who cannot
reliably walk, and transport them between seated positions at different
locations. Use of these devices still, however, requires in-person care; the
caregiver has to lift the patient into and out of the lift, and to operate the
lift’s movement. More recently, robotic devices have been developed to provide
support to lift and lower a patient in/out of a seated position, such as the
Fuji Hug [4]. To use such a device, the caretaker assists the patient in
getting in the robot, which lifts the patient into an immobilized position;
then, the caretaker moves robot and patient together. While this does
significantly reduce caretaker physical effort, it still requires sustained
in-person interaction between patient and caregiver, which can be dangerous
during epidemics.

We seek to enable methods of remote care in which a caretaker need not
physically contact an elderly patient to assist with everyday actions. An
elderly support robot would allow a caregiver or medical professional to
remotely monitor the patient and remotely control the robot. No direct
physical interactions between caregiver and patient are involved. However, the
cognitive workload could increase in operating the machine remotely, while
physical workload reduces. Furthermore, it is a challenge to provide the same
level of quality care services from a remote site. A fundamental issue in
introducing a robotic aid is whether older adults accept to receive physical
aids from robotic systems. Older adults may fear robotic aids. Physical aids
cannot be provided safely and effectively, unless the older adult accepts the
aid and behaves cooperatively. Care must be taken before executing a physical
aid; the robotic system must make sure whether the older adult is ready to
work with the robotic system; in other words, whether the older adult is
cooperative.

This opens up a new research theme on human-robot interactions. Unlike the
traditional framing of human-robot interaction problems [5, 6, 7, 8], the
behavior of a human can be completely different depending on the conditions of
the human. An older adult may be cooperative or non-cooperative depending on
his/her perception, mood, and other factors, which we describe as mental
state. In the traditional setting, there is a premise or a consensus between a
robot and the human, sharing a mission, task goals, and/or desired states. The
challenge of eldercare is that it is uncertain how the human behaves; whether
the human agrees and accepts the robotic aid, and cooperates with the robotic
system. The robotic system must estimate and ensure the internal mental state
of the human.

In this context, two major challenges must be addressed in eldercare robotics.
One is to estimate the mental state of an older adult through communication
and interactions prior to execution of a physical aid. The other is to guide
an older adult to be cooperative, persuading, convincing, or changing his/her
mental state to agree, accept, and cooperate with the physical aid. Verbal
communication, hand gestures, physical demonstration, and other means of cues
must be given to older adults in order to guide their mental state. In an
attempt to establish a new methodology, the current work presents a simple
modeling and estimation framework in the specific context of sit-to-stand
assistance. In the following, we will discuss how an older adult’s behavior
may differ depending on his/her mental state, how the characteristic behavior
difference can be detected and, through verbal and light physical cues, how
the mental state can be guided and confirmed. Based on these arguments, a
simple model and estimation method will be presented. Initial human subject
tests are conducted, and the proposed method is evaluated.

##  II Caretaker–Care Recipient Interactions in Sit-to-Stand Assistance

This section discusses interactions between a caretaker and a care recipient
(i.e., an older adult to receive physical assistance). Assistance in the sit-
to-stand transition is considered as an exemplary case study (see Fig. 1). A
caretaker needs cooperation from the care recipient for this class of physical
assistance. The caretaker cannot execute a task without gaining a cooperative
attitude and behavior from the care recipient [9]. It is rude and even
dangerous, if the caretaker abruptly yanks the older adult on his/her feet.
During the sit-to-stand transition, the care recipient first leans forward and
bends the torso, so that the Center of Mass (CoM) of the upper body goes over
the Base of Support (BOS) of the feet [10]. This allows the care recipient to
maintain balance when leaving the seat. Instead, if the caretaker pulls the
older adult although the CoM is away from the BOS, the older adult may feel
that the caretaker is acting brutishly and dangerously, and may resist being
assisted. In the case of a support robot, the older adult may be scared if the
robot’s action is unexpected and he/she is not ready to receive an assistive
action. These caretaker-care recipient interactions must be analyzed and
understood in order to develop a functional support robot.

We begin with investigating the current practice of in-person assistance, and
then derive a model describing the caretaker – care recipient interactions.
Eldercare handbooks and caretaker training manuals are useful resources for
understanding the procedure and skills required for assisting older adults[11,
9, 12]. Based on years of experience in assisting various older adults with
diverse physical and cognitive abilities, key techniques and specific
procedures have been documented in these references [3]. A few important
points we can learn from these are:

  * •

In providing older adults with physical assistance, it is critically important
to explain the goal and procedure of assisting action; what the caretaker will
be doing and what the older adult should expect. Before taking a physical
action, the caretaker must let the older adult prepare for the action.

  * •

It is not likely that an older adult voluntarily takes a cooperative action,
e.g. bending forward, following the caretaker’s verbal explanation. Rather,
older adults begin to take cooperative actions in response to the caretaker’s
physical cues, e.g. gently pushing the back of the older adult. Physically
touching, holding, and/or pushing the body of care recipient must be combined
with verbal communication.

  * •

The caretaker must assure that the care recipient is engaged and cooperative
before taking the action. The caretaker must guide the care recipient with
verbal communication and physical cues, so that he/she can be cooperative and
ready for receiving assisting actions.

The most challenging is to confirm whether the care recipient is cooperative
and ready. This is to estimate the internal mind or mental state, indicating
to which degree the care recipient is engaged, cooperative, and accepting
being assisted. This mental state cannot be measured directly. However, in the
context of sit-to-stand transition, it is conceivable that the care
recipient’s behaviors and responses to verbal and physical cues reflect the
mental state of the care recipient. For example, as a caretaker guides the
care recipient and gently pushes their back in order to prompt the care
recipient to bend forward, the care recipient may comply to the gentle push if
their own mental state is engaged and cooperative. If not cooperative and not
feeling comfortable with the physical assistance, he/she may resist against
being pushed and may even try to bend backward. Such physical responses to
physical cues may reveal the mental state of the care recipient. In the
current work, we hypothesize that cue–response behaviors reflect the mental
state of a care recipient.

Figure 1: Illustration of the physical and verbal cues which are used by human
caregivers to prepare a patient for a motion Figure 2: Illustration of the
three states of cooperativeness, as they connect cues to their corresponding
responses.

##  III Modeling

Based on the hypothesis on cue–response behaviors reflecting a mental state,
we aim to translate the current practice and skills of sit-to-stand assistance
into a computable model to be identified from experimental data. The model
will be used for guiding and estimating the cooperativeness mental state of a
care recipient.

First, we represent the level of cooperativeness with a scalar variable $\xi$:

$\xi=\begin{cases}+1&\text{: Completely Cooperative}\\\ 0&\text{: Neutral /
Unresponsive}\\\ -1&\text{: Completely Uncooperative}\end{cases}$

This internal mental state influences observable behaviors in response to a
caretaker’s cues. For the sit-to-stand assistance, we consider a gentle push
of the care recipient’s back as a physical cue given by a caretaker. Thus, the
physical cue $c_{p}$ has a unit of force.

In an attempt to connect the internal mental state $\xi$ to observable
behaviors in response to physical and verbal cues, we elaborate a model
representing the relationship based on the following considerations:

Model Considerations

  1. 1.

The human acts voluntarily to move their actual position $x$ to their intended
position $\eta$.

  2. 2.

Cooperativeness $\xi$ affects how the cues $c_{p}$ and $c_{v}$ lead to changes
in the human’s intended position $\eta$. In the cooperative case ($\xi=1$),
the change of $\eta$ induced by a cue is in the same direction as the cue. The
opposite is true for the uncooperative case ($\xi=-1$). A cue and the
resultant change in $\eta$ are in the opposite directions. In the neutral, or
unresponsive case ($\xi=0$), cues do not affect $\eta$.

For Consideration 1, we can apply a neuromotor control and biomechanic model.
Considering only 1 dimensional motion of the torso bending,

$\ddot{x}=-\lambda_{1}\dot{x}+k_{1}\left(\eta-x\right)+k_{2}\,c_{p}+w_{x,t}$
(1)

where $x$ is displacement of the torso, $c_{p}$ is the force applied by a
caretaker, and $w_{x}$ is process noise. The physical sense of the three major
terms is:

  * •

$-\lambda_{1}\dot{x}_{t}$ \- The effect of damping. Parameter $\lambda_{1}$ is
the damping rate of physical motion.

  * •

$k_{1}\left(\eta_{t}-x_{t}\right)$ \- The voluntary acceleration of the care
recipient, with the purpose of making $x\to\eta$. This is of the form
suggested by the equilibrium-point hypothesis of human motor control [13], and
is equivalent to proportional control. Parameter $k_{1}$ is the
proportionality constant.

  * •

$k_{2}\,c_{p}$ \- The involuntary acceleration due to being pushed by a
physical cue. Parameter $k_{2}$ is effective stiffness constant for this
interaction.

The above equation is in the physical domain, hence no verbal cue is involved.
The physical cue, although small in magnitude, is involved in pushing the
system. Parameters $-\lambda$, $k_{1}$, and $k_{2}$ represent the impedance of
the motor control system. In summary, the parameters for this physical model
are collectively represented as:

$\theta_{dyn}=\left\\{\lambda_{1},k_{1},k_{2}\right\\}$ (2)

For Consideration 2, the intended position is generated in response to both
verbal and physical cues. Unlike physical cues, verbal cues cannot be
represented as continuous physical values. Rather, they are symbols and their
effect upon changes to intended position $\eta$ is better represented in
discrete time.

$\eta_{t+1}=f_{int}\left(\eta_{t},c_{p,t},c_{v,t},\xi_{t};\theta_{int}\right)+w_{\eta,t}$
(3)

where subscript $t$ represents discrete time step, $f_{int}$ is a function
with parameters $\theta_{int}$, and $w_{\eta,t}$ is noise.

This transition of the intended position significantly differs depending on
the mental state. As an example embodiment of this relationship, we can
consider the following simple model where the variable of the mental state is
multiplied to the combined term of the verbal and physical cues.

$\eta_{t+1}=\eta_{t}+\xi_{t}\cdot\left(k_{3}\,c_{p,t}+k_{4}\,c_{v,t}\right)dt+w_{\eta,t}$
(4)

There are two similar non-noise components to the $\eta$ update:

  * •

$k_{3}\,\xi_{t}\,c_{p,t}$ \- The effect of a physical cue $c_{p}$ on the
change of intended position $\eta$. Parameter $k_{3}$ is the effective gain of
physical cues here.

  * •

$k_{v}\,\xi_{t}\,c_{v,t}$ \- The effect of a verbal cue $c_{v}$ on the change
of intended position $\eta$. Parameter $k_{4}$ is the effective gain of verbal
cues here.

For both of these, cooperativeness affects this cue-resultant change of $\eta$
in the following ways:

  * •

$\xi=1$ / Cooperative: Cues to move in a given direction change the human’s
intended position in the same direction.

  * •

$\xi=-1$ / Uncooperative: Cues to move in a given direction change the human’s
intended position in the opposing direction.

  * •

$\xi=0$ / Unresponsive: Cues to move in a given direction do not change the
human’s intended position.

These behaviors are illustrated in Figure 2. In summary, the parameters for
this model stage are the following:

$\theta_{int}=\left\\{k_{3},k_{4}\right\\}$ (5)

Finally, another model is necessary to represent how the mental state of
cooperativeness is changed or guided towards the cooperative state by applying
both physical and verbal cues.

$\xi_{t+1}=f_{coop}\left(\xi_{t},c_{p,t},c_{v,t};\theta_{coop}\right)+w_{c,t}$
(6)

where $f_{coop}$ is a function of the current mental state $\eta_{t}$, and
physical and verbal cues, and contains parameters $\theta_{coop}$, and
$w_{c,t}$ is noise.

The proposed model serves to elucidate how the observable behaviors of a care
recipient reflect the internal mental state of cooperativeness at three levels
of physical and mental dynamics. If the sole objective is to let a care
recipient take a bending posture, there is no need to estimate the internal
mental state. Simply measuring the bending angle, the caretaker could execute
an assisting procedure. However, the caretaker – care recipient relationship
is more complex. The caretaker would make a costly mistake if he/she
misunderstands that the care recipient is ready and cooperative. Two failure
scenarios for such a naïve method are:

Potential Failure Scenarios

  * •

As the caretaker applies a force, i.e. pushing the back of the care recipient,
he/she may bend forward if the force is significantly large and/or the care
recipient is too infirm to resist against the force. Although the care
recipient is not ready or not accepting the physical assistance, he/she may
bend forward.

  * •

Due to uncertainties at all three levels, the care recipient may exhibit a
cooperative behavior although he/she has not yet been ready.

Simply detecting a body movement alone may not be a reliable method for
determining the cooperativeness state. The method should be robust against
uncertainties. The judgement must be made not merely based on a snapshot
observation; a series of observations and previous history of individual care
recipients must be incorporated and exploited for reliable judgement. The
proposed model provides us with a framework for constructing a robust
estimator.

##  IV Cooperativeness Estimation Using a Kalman Filter with Thresholding

A simplified linear model is used for constructing a Kalman Filter for
estimating the cooperative mental state. Namely, we use (1) for the
biomechanic model and (4) for the intention model. As for the mental state
transition, we use the following random process:

$\xi_{t+1}=\xi_{t}+w_{\xi,t}$ (7)

Due to only focusing on cooperativeness estimation, not cooperativeness
prediction, for this portion of the analysis, a naïve model is used for the
cooperativeness model stage. Ideally, a rich predictive model for
cooperativeness could be used to bolster the effectiveness of cooperativeness
estimation; however, given the current lack of such a predictive model, the
naïve random process of (7) can serve as a placeholder.

In order to run the Kalman filter, we can assemble the following linear time-
variant state-space model to reflect our simplified version of the three-stage
model:
State Update Model

$\bm{x}_{t+1}=A\left(c_{p,t},c_{v,t}\right)\,\bm{x}_{t}+B\,c_{p,t}+w_{t}\text{
(noise)}$ (8)

where the state vector is
$\bm{x}_{t}:=\begin{bmatrix}x_{t}&\dot{x}_{t}&\eta_{t}&\xi_{t}\end{bmatrix}^{T}$,
and the time-dependent parameter matrices are constructed from the three
governing equations.
Measurement Model

$y_{t}=H\bm{x}_{t}+v_{t}\text{ (noise)}$ (9)
$H=\begin{bmatrix}1&0&0&0\end{bmatrix}$ (10)

####  IV-1 Cooperativeness Judgment with Thresholding

The Kalman filter is a continuous-domain filter; as such, it generates a
continuous estimate $\hat{\xi}$. Nevertheless, cooperativeness $\xi$, as
defined, is discrete, with a domain of $\xi\in\\{-1,0,1\\}$. Practical
judgments of cooperativeness $\xi$ should be made discretely. To bridge this
gap, we propose the following thresholding method: at a given time after the
initial onset of an applied cue, the Kalman-filter estimated value of $\xi$ is
sampled, then compared to a threshold $\xi_{thresh}$. If
$\hat{\xi}\geq\xi_{thresh}$, then cooperativeness is judged to be $\xi=1$. If
$\hat{\xi}\leq-\xi_{thresh}$, then cooperativeness is judged to be $\xi=-1$.
If $-\xi_{thresh}<\hat{\xi}<\xi_{thresh}$, then cooperativeness is judged to
be $\xi=0$. This scheme is illustrated in Fig. 3.

Figure 3: Demonstration of the judgment threshold. Here it is performed 0.7
seconds after the initial cue (1.2 seconds on the time axis), with a threshold
of $\xi=\pm 0.3$. The cooperativeness is judged to be $\xi=1$ (lightest gray
region).

##  V Experimentation

We sought to verify the efficacy of this Kalman-Filter-based cooperativeness
estimation technique, using basic experimental testing. In this stage of
experimental testing, we focused on characterizing response to visual and
physical cues given by a human caregiver to a human test subject. This test is
meant to be the basis for development of sensing methods that could be used by
a robotic elderly assist device; as such, though the cues were given by a
human, we limited the observables to those that could be measured by a robotic
system. As the main purpose of these experiments is to reinforce the
mathematical basis for a cooperativeness estimation algorithm, not to
establish the actual behavioral patterns of potential patients, we deemed it
unnecessary to request the participation of elderly or physically disabled
test subjects. We ran the following test on four healthy young adult human
subjects:

###  V-A Experimental Procedure

Figure 4: Left: Illustration of the first stage of experimental setup used to
collect human cue response data. Right: Video frame of the experimental
procedure, taken during the execution of an experimental trial.

Figure 5: The emojis used to communicate to the test subjects the approximate
meanings of the three cooperativeness states. Left to right: Cooperative,
Unresponsive, Uncooperative

This procedure was determined to be exempt by MIT’s IRB, COUHES, on grounds of
being a benign behavioral intervention (MIT COUHES Exempt ID E-3420).

For reference, this experimental setup is illustrated and photographed in Fig.
4.

Before the test, the human test subject is explained the general purpose of
the experiment, but not the details of the cue response model. The human test
subject is seated on a stool. To request a specified cooperativeness state of
a given trial, the experimenter provides the human test subject with a card
which displays the word “Cooperative”, “Unresponsive”, or “Uncooperative”, as
well as an emoji which approximately represents the emotional context of the
requested cooperativeness state (see Fig. 5). The subject is NOT explained the
modeled definitions of the cooperativeness states, in the concern that
awareness of the model may influence their behavior.

The experimenter then proceeds to provide the test subject with a physical
cue: a push forward to the upper back, a verbal cue: a command to move forward
or backward, or both. The human test subject responds as they see fit, given
the requested cooperativeness state provided by the experimenter beforehand.
The physical cue is delivered though an Optoforce compressive force sensor
held by the experimenter; thus, the force magnitude of the physical cue is
measured. The timing and direction of the verbal cue is recorded using a
microphone. The response of the human test subject is characterized in terms
of the horizontal position of their shoulder; this is measured using a video
camera, which tracks a visual target taped to the subject’s shoulder.

###  V-B Experimental Parameters

Four subjects were tested, in a series of 25 different trials. Each of the 25
trials was randomly assigned one of each of the following properties:

  * •

Requested Cooperativeness: $\xi\in\\{-1,0,1\\}$

  * •

Physical Cue Intensity: {None, Soft, Hard}

  * •

Verbal Cue: {None, Move Back, Move Forward}

  * •

Relative Timing Between Physical and Verbal Cues (if both):
$\\{-4,-3,-2,-1,0,1,2,3,4\\}$ seconds

The same set of trial parameters was used for each human test subject.

###  V-C Experimental Results

####  V-C1 Kalman Filter Performance Analysis

The results of a single trial are shown in Figure 6. In this trial, Subject 1
was requested to be cooperative ($\xi=1$). Subsequently, a hard forward
physical cue was applied, immediately followed by a verbal cue to move
forward. Subject 1 was accelerated directly by the push, and then proceeded to
move themself forward further. As shown in Fig. 6, the Kalman filter estimates
two pulses in intended position $\eta$, an initial large, brief pulse
corresponding to the initial acceleration, and a second smaller, sustained
pulse corresponding to the additional motion. This two-pulse estimation of
intended position $\eta$ leads to a similar two-pulse estimation of
cooperativeness $\xi$, which briefly goes negative between the pulses.

This brief negative estimate of $\xi$ (which should be $\xi=1$ throughout) is
due to one of the main disadvantages of this Kalman filter: it cannot
perfectly distinguish the direct dynamic motion caused by hard physical cues
from the indirect voluntary motion caused through cooperativeness. While
$k_{2}\,c_{p}$, the direct mechanical compliance term of the dynamics equation
(1), does serve to predict the $x$ motion directly caused by a physical push,
any variation in the effective mechanical compliance of the human subject will
result in $x$ motion being attributed to intended motion $\eta$ instead. This
can be seen in Fig. 6, where the initial acceleration is attributed to an
unusually large spike in intended position $\eta$ of 1 meter. Nevertheless,
the fact that Subject 1 does not resist the motion results in their
cooperativeness being correctly estimated as $\xi=1$ by the threshold
judgment. The threshold judgment for all data was made at a delay of 0.75
seconds after cue initiation, and a threshold of $\xi=\pm 0.3$.

Figure 6: Response of Subject 1 to both a forward push cue and a “Move
Forward” verbal cue, with a requested cooperativeness of $\xi=1$. Given the
measured cues $c_{p}$ and $c_{v}$, and measured position $x$, the human
subject’s modeled position $x$, velocity $\dot{x}$, intended position $\eta$,
and cooperativeness $\xi$ are estimated using a modified Kalman filter. The
thresholds and time window for cooperativeness judgment are shown in the plot
for estimated $\xi$.

####  V-C2 Aggregate Analysis of Cooperativeness Judgment

TABLE I: Number of trials across all test subjects ($N=100$), sorted by their
requested (true) value of cooperativeness $\xi$ and their estimated
cooperativeness $\xi$, as estimated by the Kalman filter with threshold
judgment.

While only one trial is presented in Fig. 6, there were 100 different trials
actually performed across the 4 human test subjects. For the sake of brevity
and ease of reading, aggregate data is presented only in Tables I–III. Table I
shows the overall results of the thresholded Kalman filter cooperativeness
judgment scheme; specifically, it sorts the 100 trials in terms of the
cooperativeness value requested of the test subject (the effective ground
truth) and the cooperativeness value estimated by the judgment scheme. This
table effectively shows the joint sample distribution of requested $\xi$ and
estimated $\xi$.

TABLE II: Conditional sample distribution of estimated cooperativeness $\xi$,
conditioned on the requested (true) cooperativeness $\xi$

These results can also be analyzed in terms of conditional sample
distributions. Table II shows the sample conditional distribution of estimated
$\xi$ given requested $\xi$. In other words: if a trial if known to have a
certain requested $\xi$, what is the sample probability of it having been
estimated to be a certain value? The judgment scheme is very good at judging
unresponsive cases to be unresponsive, at a rate of 79.2%. An uncooperative
cases is most likely to be judged as unresponsive, with a dismal correct rate
of judgment of 34.4%. Cooperative cases are most likely to be judged as such,
with a correct rate of judgment of 52.3%.

TABLE III: Conditional sample distribution of requested (true) cooperativeness
$\xi$, conditioned on the estimated cooperativeness $\xi$

The dual conditional analysis also sheds some valuable light. Table III shows
the sample conditional distribution of requested $\xi$ given estimated $\xi$.
In other words: if a trial is estimated to have a given $\xi$, what is the
sample probability of it having actually been requested to be a given value
$\xi$? The judgment scheme is relatively trustworthy in judgments of being
cooperative, with a $92\%$ true positive rate. Judgments of uncooperative are
more likely than not to actually be uncooperative, with a $68.8\%$ true
positive rate. Judgments of unresponsiveness mean effectively nothing,
however, with a roughly equal chance of having been caused by any requested
cooperativeness.

##  VI Conclusion

This paper has addressed one of the key challenges in applying robotic systems
to the eldercare environment. To be viable, the technology must be accepted by
elderly people, and robots must be able to gain a cooperative behavior from
the elderly. We have presented a basic modeling framework for estimating the
mental state of cooperativeness in the context of sit-to-stand assistance.
From existing literature and practical eldercare knowledge, we have found that
the internal mental state of an older adult is reflected to his/her observable
behaviors in response to verbal and physical cues given by a caretaker. Based
on this, a mental-physical model has been constructed at three levels. The
lowest is a biomechanic, neuromotor control model relating observable
behaviors to his/her intended posture and movement. The highest is a mental
model indicating his/her cooperativeness, which can change depending on a
caretaker’s guidance, i.e. verbal and physical cues. The middle layer is a
model linking the mental state to intended posture and movement. Based on this
model, a thresholded Kalman filter judgment scheme has been constructed to
estimate the mental state from observable body responses to a caretaker’s
cues. IRB-approved human subject tests were conducted to verify the efficacy
of this judgment algorithm in healthy adults. The thresholded Kalman filter
cooperativeness judgment scheme proposed in this work successfully identified
cooperative response, with a 92% true positive rate.

## References

  * [1] S. H. Yi, I. See, A. G. Kent, N. Vlachos, J. C. Whitworth, K. Xu, K. A. Gouin, S. Zhang, K. J. Slifka, A. G. Sauer, P. Kutty, J. F. Perz, N. D. Stone, and M. J. Stuckey, “Characterization of COVID-19 in Assisted Living Facilities — 39 States, October 2020,” _MMWR. Morbidity and Mortality Weekly Report_ , vol. 69, 2020. [Online]. Available: https://www.cdc.gov/mmwr/volumes/69/wr/mm6946a3.htm
  * [2] “Nearly One-Third of U.S. Coronavirus Deaths Are Linked to Nursing Homes,” _The New York Times_ , June 2020. [Online]. Available: https://www.nytimes.com/interactive/2020/us/coronavirus-nursing-homes.html
  * [3] “Arjo Patient Handling.” [Online]. Available: https://www.arjo.com/int/products/patient-handling/
  * [4] “Fuji Hug L1.” [Online]. Available: http://www.fuji.co.jp/en/items/hug/hugl1
  * [5] S.-J. Blakemore and J. Decety, “From the perception of action to the understanding of intention,” _Nature Reviews Neuroscience_ , vol. 2, no. 8, pp. 561–567, Aug. 2001, bandiera_abtest: a Cg_type: Nature Research Journals Number: 8 Primary_atype: Reviews Publisher: Nature Publishing Group. [Online]. Available: https://www.nature.com/articles/35086023
  * [6] N. Canessa, F. Alemanno, F. Riva, A. Zani, A. M. Proverbio, N. Mannara, D. Perani, and S. F. Cappa, “The Neural Bases of Social Intention Understanding: The Role of Interaction Goals,” _PLOS ONE_ , vol. 7, no. 7, p. e42347, July 2012, publisher: Public Library of Science. [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0042347
  * [7] Z. Yu, S. Kim, R. Mallipeddi, and M. Lee, “Human intention understanding based on object affordance and action classification,” in _2015 International Joint Conference on Neural Networks (IJCNN)_ , July 2015, pp. 1–6, iSSN: 2161-4407.
  * [8] L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor, “Learning Object Affordances: From Sensory–Motor Coordination to Imitation,” _IEEE Transactions on Robotics_ , vol. 24, no. 1, pp. 15–26, Feb. 2008, conference Name: IEEE Transactions on Robotics.
  * [9] C. G. Nicholl and J. Wilson, _Elderly Care Medicine Lecture Notes_ , 8th ed. West Sussex, UK: Wiley-Blackwell, 2012.
  * [10] M. E. Roebroeck, C. A. M. Doorenbosch, J. Harlaar, R. Jacobs, and G. J. Lankhorst, “Biomechanics and muscular activity during sit-to-stand transfer,” _Clinical Biomechanics_ , vol. 9, no. 4, pp. 235–244, July 1994. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0268003394900043
  * [11] M. Alwan and R. A. Felder, Eds., _Eldercare Technology for Clinical Practitioners_ , ser. Aging Medicine. Totowa, NJ: Humana Press, 2008.
  * [12] J. Wick and G. Zanni, “Wheelchair-Bound Residents in Nursing Facilities: The Basics,” _The Consultant Pharmacist_ , vol. 22, no. 2, pp. 119–139, Feb. 2007.
  * [13] A. G. Feldman, “Once more on the equilibrium-point hypothesis ($\lambda$ model) for motor control,” _Journal of Motor Behavior_ , vol. 18, no. 1, pp. 17–54, 1986, pMID: 15136283. [Online]. Available: https://doi.org/10.1080/00222895.1986.10735369