=Paper=
{{Paper
|id=None
|storemode=property
|title=Technical Approaches to Unobstrusive Geriatric Assessments in Domestic Environments
|pdfUrl=https://ceur-ws.org/Vol-678/BMI10-06.pdf
|volume=Vol-678
}}
==Technical Approaches to Unobstrusive Geriatric Assessments in Domestic Environments==
https://ceur-ws.org/Vol-678/BMI10-06.pdf
BMI'10 63 Karlsruhe, September 21th, 2010
Technical Approaches to
Unobtrusive Geriatric Assessments
in Domestic Environments
Thomas FRENKEN a,1 , Olaf WILKEN a and Andreas HEIN a
a
OFFIS - Institute for Information Technology, Escherweg 2, D-26121 Oldenburg
Abstract. Two novel approaches to implementing unobtrusive and and non-
stigmatizing geriatric assessments in domestic environments are presented. Mobil-
ity regarding self-selected gait velocity is assessed using light barriers and a two
dimensional laser range scanner. A single power sensor installed into the fuse box
helps determining self-care ability by detecting appliance usage. The described as-
sessment approaches are meant to enable early detection of disabilities and thus
prevention and provision of individual support directly in peoples’ homes. They
overcome the disadvantages of classical geriatric assessments. The technical as-
sessments contribute to the concept of Ambient Assisted Living (AAL) which tar-
gets at fostering a self-determined lifestyle in peoples’ personal homes by provid-
ing a technical infrastructure and supporting services. The assessments do not re-
quire any direct interaction with the inhabitants. Two experiments conducted in a
living lab and a residential care facility in Oldenburg, Germany demonstrate the
general feasibility of the approaches.
Keywords. assessments, mobility, laser range scanner, power sensor, activity
recognition
1. Introduction
The so called double aging of the society which can be observed in many European and
industrial countries poses many problems. While on the one hand less young people are
born, peoples’ general life life expectancy constantly increases. The resulting increase
of the old-age dependency ratio means that in the future there will be less young people
having to pay and care for more and older elderly people [7]. This trend also means that,
considering that many diseases’ prevalence increases with old age, in the future there will
be more multimorbid patients i.e. more patients suffering from more than one disease. In
Germany, nearly 40% of all people aged 40-54 years suffer from more than one diseases,
4% of those people are diagnoses to have at least five diseases. These figures tremen-
dously increase with age. Nearly 60% of people aged 55-69 years have more than one,
12% have more than five diseases. For people aged 70-85 years these figures increase to
over 70%, respectively 24% [14].
The branch of medicine concerned with the diagnosis, treatment, and prevention of dis-
1 Corresponding Author: Thomas Frenken, E-mail: thomas.frenken@offis.de.
BMI'10 64 Karlsruhe, September 21th, 2010
eases in older people and the problems specific to aging is called geriatrics. In this con-
text the term "older" often refers to people being older than 80 years or being multimor-
bid and older than 70 years. The aim of each geriatric treatment is to recover and main-
tain an independent lifestyle of patients. In difference to other branches of medicine a
detailed diagnosis of diseases is not that important and sometimes even not possible due
to the interference of diseases in multimorbid patients. The so called geriatric assessment
is a "multidimensional process designed to assess an elderly person’s functional abil-
ity, physical health, cognitive and mental health, and socio-environmental situation" [2].
Within the assessment process standardizes assessment tools like the Timed-Up-And-Go
[27] are used.
In today’s health care systems assessments are only applied in hospitals most often af-
ter acute incidents took place. This is mainly because domestic environments are only
fractionally integrated into health care systems. By assessing elderly peoples’ abilities
directly in their home environments many acute incidents may be prevented and care or
rehabilitation means may be provided according to individual needs. This may increase
concerned peoples’ perceived quality of life while saving costs.
Within this paper two technical approaches to implementing unobtrusive geriatric assess-
ments in domestic environments are presented. The approaches are exclusively based on
ambient sensors and require no interaction with the patients monitored. A laser range
scanner is used to assess peoples’ self-selected gait velocity. A power sensor integrated
into the fuse box monitors peoples’ appliance usage in order to deduce their self-care
ability. Experiments have been conducted in a living lab and a residential care facility.
2. Medical Motivation
The aim of each geriatric treatment is to recover and maintain an independent lifestyle
of patients. Therefore, instead of focusing on a detailed diagnosis, the geriatric assess-
ment concentrates on assessing an elderly person’s functional ability, physical health,
cognitive and mental health, and socio-environmental situation. This can not be done by
a single physicians but it is a multidimensional and multidisciplinary process in which
standardized tests, so called geriatric assessment tools, are utilized.
Although providing a good insight into peoples’ abilities in rather short time, there are
several drawbacks about geriatric assessments in today’s health care systems:
Place of Execution Today, geriatric assessments are only used in professional care fa-
cilities like hospitals or doctors’ practices most often after an acute incident al-
ready took place. Their potential for prevention or surveillance of rehabilitation
advances within the domestic environment is not exploited. Additionally, profes-
sional care facilities are free of environmental obstacles which leads to test results
not reflecting the performance of people in their domestic environments.
Test Awareness Geriatric assessments in professional care facilities are often perceived
as test situations which leads to people performing at their best. Again the assess-
ment results do not reflect the performance of people within everyday life.
Subjective Execution Despite providing standardized descriptions and guidance for ex-
ecution, geriatric assessment are often executed subjectively by caretakers. Small
differences in execution make comparison of results between people and between
several executions difficult.
BMI'10 65 Karlsruhe, September 21th, 2010
Required Effort At the moment there is only very little technical support for geriatric
assessments. Assessments need to be repeated several times during the treatment
in order to provide reliable results. This makes assessments, although being rather
simple tests, time- and personnel-intensive.
Two central aspects of each geriatric assessement are a patient’s mobility and self-care
ability. A person’s mobility, i.e. being able to move around and to get into and keep up
certain body positions, is a fundamental requirement for an independent lifestyle [9] and
is closely connected to his or her perceived quality of life. Starting at the age of 60 years,
elderly people expose a slower gait velocity [13]. This age-related change in mobility
is not pathological. Nevertheless, many pathologic diagnoses can be directly deduced
from an impaired mobility [3]. Gait and balance disorders have shown being related to a
higher risk of falling. The most obvious impairment visible even to layman is a reduced
self-selected gait velocity which has been found being related to an increased risk for
falls, admission to hospital, and need of care [20]. The most frequently used assessment
from the field of mobility is the Timed-Up & Go [27]. Despite being able to move around
taking care of oneself requires various cognitive and physical capabilities for executing
certain activities. Therefore, a person’s self-care ability is often assessed in terms of the
ability to execute various (instrumental) activities of daily living (ADL).
3. State of the Art
Currently, there is only very limited technical support for executing geriatric assessments
and nearly no support is used in daily clinical practice. However, due to the potential
of assessing people in their domestic environments research has investigated various
approaches utilizing different sensor technologies.
3.1. Mobility Assessments
Within hospitals, especially in case problems with prostheses or implants, laboratories
equipped with camera-based systems for cinematic gait analysis based on marker track-
ing, fluoroscopy systems, systems for cinetic gait analysis of ground reaction forces uti-
lizing force platforms, and dynamic electromyography are used.
Recent research investigated mobility telemonitoring directly in the home of affected
people using either wearable sensors or sensors installed into the environment [30].
Wearable sensors may be placed either on one or many positions directly on the body
or in cloth and objects worn. Several wearable sensors are also referred to as body area
networks (BAN). Accelerometers and gyroscopes have been applied to gait phase detec-
tion [33] and measurement of various parameters of gait like walking velocity, cadence,
average step length, and step timing variability [17,35]. Pressure sensors under or inte-
grated into the sole of shoes, later combined with accelerometers and gyroscopes, have
been used to measure pressure distribution on certain points of the feet in order to infer
gait phases or events [22,15] or to detect abnormal gait patterns [34,6].
Ambient sensors are integrated into the environment or in objects used by the person
monitored. Environments equipped with such sensors are also referred to as health smart
homes [30]. Very few systems for detailed mobility analysis based on ambient sensors
BMI'10 66 Karlsruhe, September 21th, 2010
have been described so far. Most approaches rely on home automation technology like
motion sensors, light barriers, or reed contacts placed in door frames or on the ceiling in
order to determine a person’s walking direction or velocity [4,24]. The iWalker [31] is a
mobile assessment instrument. It is equipped with optical sensors for measuring wheel
rotation and moving direction, a six-dimensional gyroscope and accelerometer measur-
ing speed and distance, several load cells in the handles and on the frame measuring
weight distribution and propulsion forces, and a portable camera for recording the envi-
ronment while walking.
In summary, miniaturized wearables can provide detailed biomechanical information
about the person wearing the device, ideally in any environment. Nevertheless, most
wearable sensors are not suitable for unsupervised use by layman. Wearables require di-
rect interaction, not or incorrectly donning the device heavily influences the measure-
ments. Studies indicate that simply being aware of wearing a sensor may influence the
measurement results [16]. Mobility monitoring utilizing ambient sensors has been rather
imprecise so far. Nevertheless, ambient sensors are totally unobtrusive and suited even
for layman. Ideally, monitored persons do not recognize present sensors in their everyday
life, thus measurements might be more reliable. Ambient sensors may monitor several
persons in their coverage at the same time. However, identifying the monitored persons
is often difficult. Installation of sensors may be costly.
3.2. Activity Recognition
Early approaches to activity recognition utilized mainly home automation sensors such as
motion sensors [19], temperature sensors, light sensors [1], and binary-state sensors [32].
More complex sensors like RFID-tags [26], vision sensors [18], body-worn accelerom-
eters [29], or microphones [5] can be used as well. Recognition of interleaved and con-
current activities, and the unsupervised learning of activities are still open research ques-
tions since the collection of good training data is difficult. In [11] skip-chain conditional
random fields (SCCRF) are applied to the recognition of interleaved activities. Detection
of concurrent activities was realized using correlation graphs. Detection accuracy was up
to 90 %. In [12] unsupervised learning was used for activity recognition. With the use
of k-means cluster and Latent Dirichlet Allocation (LDA) various activities like dinner,
commuting, lunch and office work were recognized with an accuracy of 76.9 %.
The application of power sensors to activity recognition is currently investigated. The
idea is to map appliance usage to activity execution e.g. the usage of a coffee machine
in the morning to preparing breakfast. In the area of NALM (non-intrusive load monitor-
ing), i.e. the process of analyzing the properties of the electrical energy consumption of
a house in order to identify the electrical appliances used and their energy consumption,
two different types of power sensors are used. The first type are proprietary sensors e.g.
described in [10,21,23] that have a very high sampling rate. The second type are smart
meters [28] that are produced in large quantities and are installed by energy providers
in households. This type has a low sampling rate. In [10] only devices consuming more
than 100 W were detected with a sampling rate of 1 Hz. It could not be distinguished be-
tween different states of a device. In [21] the authors describe how the different states of
a washing machine can be identified by a directly connected power sensor. In [23] a pro-
prietary high resolution sensor 5 kHz was used, resulting in a correct identification rate
of 90 %. In [28] devices could be identified with a smart meter using neural networks.
BMI'10 67 Karlsruhe, September 21th, 2010
Only appliances with a major energetic impact on the daily load shape were detected. In
an evaluation the appliances were detected with an accuracy of 90 %.
4. Approach to Unobtrusive Assessments
The concept of Ambient Assisted Living (AAL) targets at fostering a self-determined
lifestyle in peoples’ personal homes. It combines a technical infrastructure within the
home environment, consisting of sensors, actuators, and communication technologies,
with supporting services often provided by third-parties. Support within the domestic en-
vironment may be provided in various domains like communication, mobility, self-care,
or domestic life (according to the International Classification of Functioning, Disability
and Health (ICF) from the World Health Organization (WHO)). Support provided should
be as unobtrusive, individual, and non-stigmatizing as possible and should be usable with
only little technical precognition. However, in order to provide support existing impair-
ments, activity limitations, or participation restrictions (summarized as disabilities within
the ICF) first need to be detected, ideally in peoples’ homes and as early as possible.
We have developed two novel approaches to detecting disabilities in the field of mobility
and self-care ability in domestic environments. The approaches are designed to meet the
aforementioned requirements of supporting services within the AAL concept.
4.1. Mobility Assessments
Our novel approach to mobility monitoring combines two types of ambient sensors: Light
barriers for measurement of general trends in mobility mainly in the home environment
and a very precise ambient sensor, a two dimensional laser range scanner for detailed gait
analysis. We hypothesize that laser range scanners are applicable for precise measure-
ment of capacity regarding mobility in an environment mainly free of environmental in-
fluences like a hospital as well as for measuring performance in a domestic environment.
Gathered information should be sufficient for analyzing various spatio-temporal param-
eters of gait. Additionally, there is no need for the patients to interact with the sensor
which may lead to more reliable results and makes the sensor even suitable for measur-
ing mobility of cognitively impaired people. As a first step towards realizing the desired
assessment system, we have designed an algorithm for reliably and precisely computing
self-selected gait velocity in domestic environments [8]. The approach does not require
a priori knowledge about the environment.
Figure 1(a) shows the principle of computing a person’s movement trajectory from mea-
surements taken by a laser range scanner at the height of the subject’s legs. The pro-
cess of computing an approximated self-selected gait velocity from those measurements
involves three steps: environment recognition, dynamic object measurement, and gait
velocity computation:
Environment Recognition Prerequisite for measuring the movement trajectory of a
subject is the ability to distinguish moving objects like humans from stationary ob-
jects. This is achieved by computing a histogram of measurements for each mea-
surement angle α in the measurement sector [startα , endα ] over a given number
k c of measurement sets including only the static environment.
BMI'10 68 Karlsruhe, September 21th, 2010
covered
T0 * (k+1)
footstep front door
wardrobe
͢ bedroom door
trajectory rk+1 half opened
defined by bathroom
measured data ͢
dk
T0 * k
͢
rk
bedroom
footstep
2nd light barrier
T0 * (k-1)
͢
trajectory rk-1
defined by rk,α
subject‘s laser range scanner
1st light barrier
center of mass laser range scanner livingroom
(a) Principle of computing gait velocity from (b) Laser range scanner’s measurements for three
laser range scanner’s measurements measurement sets of the second experiment displayed
into the living lab’s floor plan
Figure 1. Utilizing a Laser Range Scanner for Computing Self-selected Gait Velocity and for Visualizing
Movement Trajectories
Dynamic Object Measurement During the measurements the mean range r̄c (α) and
standard deviation σ c (α) for each measurement angle stored within the histogram
are used to subtract measurements representing background from the foreground
i.e. the legs of a moving person. All range measurements rk,α outside the his-
togram’s interval [r̄c (α) − σ c (α), r̄c (α) + σ c (α)] represent a dynamic objects and
thus a person’s legs.
Gait Velocity Computation For each measurement set k the mean range vector ~r̄k is
computed from all foreground measurements. The computed mean range vector
represents the approximated center of mass of the measured person. The distance
walked dk between two measurement sets k and k − 1 is approximately the length
of the vector d~k between two consecutive mean range vectors ~r̄k and ~r̄k−1 . dk
by the time elapsed T0 ∗ k − T0 ∗ (k − 1) = T0 between the two corresponding
measurement sets k and k−1 gives approximately the self-selected gait velocity vk
for point in time T0 ∗k. Applying an additional mean filter to all computed velocity
values vk within one second gives the approximated gait velocity per second.
4.2. Activity Recognition
Our novel approach to activity recognition utilizes a single power sensor installed into
the fuse box of a house or flat (one for each power circuit). Activities are detected by
mapping these to sequences of appliance usage which are detected by the power sensor.
Based on the measurement of electrical parameters by the power load sensor the on- and
off-switches of electrical appliances can be identified. The electrical parameters voltage
u(t), current i(t) are measured and sampled at a frequency of 17 Hz. Devices are identi-
fied by their characteristic switching operations. An example of a characteristic signal of
devices is shown in figure 2(b). The overall principle of the approach is shown in figure
2(a). It consists of three levels:
Identification/Filtering First, running appliances are identified. Concurrently running
appliances have a different voltage drop compared to a single one since each
BMI'10 69 Karlsruhe, September 21th, 2010
(a) Principle of activity recognition using electrical (b) Current curve measured by the power load sensor
parameters
Figure 2. Utilizing a Power Sensor for Activity Recognition
switch-on of an appliance produces a small voltage drop. Therefore, electrical pa-
rameters such as the real power, which is dependent on the voltage, is not a well-
defined feature. Resistances are the basis for extracting features of each appliance
since they remain constant for concurrently running appliances. The features mean
(R), covariance (Cov) and phase ϕ are extracted from the switched-on devices.
For switch-off, only the features’ mean values are extracted. For the correct iden-
tification of concurrently running appliances these have to be switched on one af-
ter another with a delay of at least 1.5 seconds. Currently, a supervised learning
technique is applied for learning the appliances’ characteristic features (profiling).
After the classification (nearest neighbor method, example in figure 3(a)) the states
of appliances with user interaction have to be separated from those without inter-
action (e.g. turn on/off of the refrigerator is a interaction without a user).
Activity Recognition In the second level the switch on/off sequences of appliances are
assigned to activities. For the sequence analysis algorithms from the field of bioin-
formatics are applied. In figure 5 an example of a sequence pattern is shown. The
letters represent the states of different appliances. For example the letter "P" stands
for a switched on cooker. Patterns may change throughout different days (e.g.
weekdays/weekends) and time of day.
Assessment and Detection of Behavioral Change In the third level the identified ac-
tivities are qualitatively analyzed and mapped to geriatric assessments. Further-
more, changes in activity patterns are detected over time and are transfered. Defi-
ciencies in certain activities are detected and allow to assess a inhabitants self-care
ability respectively his or her requirement for supporting services.
5. Experiments
Approaches are still within an experimental state. Initial experiments to proof the feasi-
bility of the developed approaches have been conducted in a living lab and in an apart-
ment located in a residential care facility in Oldenburg, Germany.
BMI'10 70 Karlsruhe, September 21th, 2010
bathroom
living room
bedroom
entrace hall
kitchen
(a) Feature space and clusters of five different appli- (b) Floor plan of the apartment with available appli-
ances during a switchon operation ances used for the experiment with the power sensor
Figure 3. Clustering of Appliances and Floor Plan of the Apartment for the Conducted Experiment
5.1. Mobility Assessments
An experiment for comparing feasibility and precision of measuring self-selected gait
velocity using light barriers and a laser range scanner was conducted. Five healthy peo-
ple aged 25-39 years participated in the experiment. The laser range scanner was placed
at a height of 38cm in the entrance hall of the flat. The light barriers were mounted to the
door frames in the living room and the bedroom. Doors to living room and bedroom were
open, front door and bathroom door were closed. On- and off-states of the light barriers
were wirelessly transmitted to an FHZ1000PC FS20 base station. The FHZ1000PC and
the Hokuyo URG-04LX-UG01 laser range scanner were connected to a PC using the
USB port.
For each participant ten measurement sets were recorded while walking along two paths
within the flat’s entrance hall (figure 1(b)). For the first part of the experiment partici-
pants were asked to walk directly from the living room to the bedroom and vice versa.
For the second part, participants had to walk from the living room to the front door,
lock the front door, and then walk to the bedroom. On their way back they were asked
to reopen the front door before entering the living room. Participants were instructed to
walk at their normal speed. The time to walk the paths was recorded directly by the laser
range scanner, by computing the time between reception of "on" states of the light barri-
ers, and manually using a stopwatch. The manual measurement is thought to be the gold
standard since it is commonly used in clinical environments. The distance walked was
again measured by the laser range scanner and thought to be apriori knowledge for the
computations based on the time measurements of the light barriers and the stopwatch.
Results show (figure 4(a)) that during the first part of the experiment (walking straightly
from living room to bedroom and vice versa) self-selected gait velocity could be pre-
cisely computed from measurements of light barriers as well as from those of the laser
range scanner compared to the stopwatch measurements. Mean difference compared to
the gold standard was only 0.023m/s for the light barriers and 0.063m/s for the laser range
scanner. Standard deviations were 0.05m/s respectively 0.10m/s. Mean self-selected gait
velocity across all participants was 0.99m/s, ranging only from 0.90m/s for the first par-
ticipant to 1.1m/s for the third participant. The second part of the experiment was con-
ducted in order to demonstrate the advantages of using a laser range scanner’s measure-
ments for computing self-selected gait velocity. Computations based on the time mea-
BMI'10 71 Karlsruhe, September 21th, 2010
1.5
light barriers
laser scanner
stopwatch
1 2.
gait velocity (m/s)
1.
0.5
covered footstep
Noise measured
3. during 2.
laser range scanner
0
1 2 3 4 5
participant
(a) Computed gait velocity for first experiment (b) Noise and occlusion in laser range scanner mea-
surements
Figure 4. Results and Problems of Computing Self-selected Gait Velocity From Range Measurements
sured by the light barriers and the stopwatch for the second part were very imprecise due
to the participants not walking directly from the living room to the bedroom but standing
still in between while opening or locking the front door. There is no possibility to com-
pensate for this using only light barriers on room doors and in a real setup there would
even be no chance to detect the longer walking distance. However, using the measure-
ments of the laser range scanner, self-selected gait velocity could be computed precisely
even for the second part of the experiment. This was achieved by removing all distance
measurements from the computation whose difference vectors were smaller than a de-
fined threshold and did thus represent rest. By filtering those measurements computed
self-selected gait velocity for the second experiment had a mean error of only 0.01m/s
and a standard deviation of 0.22m/s compared to the computation for the first experiment
based on the laser range scanner’s measurements.
Depending on the position of the laser range scanner relative to the subject measured,
trajectory and gait velocity computed based on the measurements may differ from the
trajectory defined by the subject’s center of mass and the actual gait velocity due to two
reasons. First, the laser range scanner measures only the surface of legs facing the scan-
ner (figure 4(b), case 1). The scanner is not capable of measuring the depth of an ob-
ject. Therefore, the computed mean distance vector will point to a position slightly more
into the direction of the laser range scanner than the vector pointing to the real center of
mass. The difference in these two vectors depends on the depth of the object measured.
Second, looking from the position of the laser range scanner one leg may be covered
by the other while walking (figure 4(b), case 3), especially during the gait phases mid
stance and initial swing of the human gait cycle [25]. The computed mean distance for
such measurements differs from those including both legs resulting in either a longer or
shorter mean distance depending on the leg covered. Intermittent noise (figure 4(b), case
2) does not heavily influence the measurement results.
5.2. Activity Recognition
Within a field study over a period of six months the system was installed in an apartment
(floor plan in figure 3(b)) inhabited by an elderly person. A total of about 30 appliances
BMI'10 72 Karlsruhe, September 21th, 2010
Figure 5. Recorded Appliance Sequence and Identified Activities From the Experiment
were available in the apartment. In the course of data collection some problems occurred
in contrast to laboratory studies like signal noise of appliances and replacement of elec-
trical appliances the system was unaware of. The accuracy in the detection of devices was
about 80 %. For the unsupervised learning of activities it is important to filter as much
noise as possible like visiting hours and absences times of the inhabitant. Finite state
machines have been applied for filtering. The absences have been detected with 78 %
and the visiting hours with 67 %. In figure 5 the result of unsupervised learning activities
over a period of four days is shown. The individual sequences are different activities.
6. Conclusion
In the near future health systems will have to cope with more elderly and thus multi-
morbid patients. The medical branch of geriatrics deals with these patients respectively
with diseases and problems specific to aging. The aim of each geriatric treatment is to
recover and maintain an independent lifestyle as long as possible. Required support or
compensation is determined within the geriatric assessment respectively by utilizing var-
ious geriatric assessment tests. The concept of Ambient Assisted Living (AAL) targets
at fostering a self-determined lifestyle in peoples’ personal homes. It combines a tech-
nical infrastructure within the home environment with supporting services. Therefore,
bringing geriatric assessments to the home environment seems to be reasonable in order
to provide prevention and to determine existing disabilities and thus required support.
Support provided should be as unobtrusive, individual, non-stigmatizing as possible and
should be usable with only little technical precognition.
Within this paper we have presented two novel approaches to implementing unobtrusive
and non-stigmatizing assessments in the domestic environment. Light barriers and a laser
range scanner have been used to reliably and precisely compute self-selected gait veloc-
ity. The conducted experiment suggests that light barriers shall be used for measuring
general trends in mobility covering a whole flat while the laser range scanner provides
detailed measurements in a smaller area and in professional care facilities. The computa-
tion works without a priori knowledge of the environment. A power sensor installed into
the fuse box of homes helps detecting appliance usage of inhabitants. By determining
activities carried out and anomalies regarding learned normal behavior self-care abilities
may be assesses. The experiment has revealed problems during the measurements like
signal noise of appliances and replacement of electrical appliances the system was un-
aware of. The technically supported assessments overcome the disadvantages of classical
assessments. They may be utilized directly in the home environment enabling prevention
and surveillance of rehabilitation advance. Since the used technology is totally unobtru-
BMI'10 73 Karlsruhe, September 21th, 2010
sive, technically supported assessments are ideally not perceived as test situations and do
provide a realistic insight in peoples’ everyday performance in their natural environment.
The sensors measure objectively, continuously, and do not require any direct interaction
with the inhabitant. However, it remains to be investigated how to exactly relate mea-
surement results to results of established geriatric assessment.
The conducted experiments have demonstrated the general feasibility of the presented
approaches. We are currently planning to evaluate the assessments in a assisted living fa-
cility. We are also working on enhancing the gait velocity computation by incorporating
background knowledge on human gait and want to compute additional spatio-temporal
parameters of gait by applying object identification techniques to the scanner’s measure-
ments. These parameters may be used for detection of abnormal gait and may be incor-
porated into a model of gait. Regarding assessment of self-care ability we are working
on models for detection of interleaved and concurrent activities and want to investigate
usage of more sophisticated unsupervised learning techniques.
Acknowledgments
This work was funded in part by the German Ministry of Education and Research within
the research project "PAGE" (grant 01FCO8044) and in part by the Ministry for Science
and Culture of Lower Saxony within the Research Network "Design of Environments for
Ageing" (grant VWZN 2420).
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