Stair Climb Power Measurements via Inertial Measurement Units

Towards an Unsupervised Assessment of Strength in Domestic Environments

Sandra Hellmers

, Tobias Kromke

, Lena Dasenbrock

, Andrea Heinks

urgen M. Bauer

, Andreas Hein

and Sebastian Fudickar

Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Heidelberg University, Agaplesion Bethanien Krankenhaus Heidelberg, Heidelberg, Germany

Keywords:

Stair Climb Power, Inertial Measurement Unit (IMU), Power, Stair Ascending, Machine Learning, Clinical

Assessment, Unsupervised, Wearable Sensors.

Abstract:

In order to initiate interventions at an early stage of functional decline and thus, to extend independent living,

the early detection of changes in functional ability is important. The Stair Climb Power Test (SCPT) is a

standard test in geriatric assessments for strength as one of the essential components of functional ability. This

test is also well suited for regular and frequent power measurements in daily life since the activity of climbing

stairs is usually frequently performed.

We introduce an automated assessment of the SCPT based on inertial measurement units (IMU) in a study

of 83 participants aged 70-87 years. For power evaluations of the lower extremities, the activity of climbing

stairs was automatically classiﬁed via machine learning and the power was calculated based on the test duration

and covered height. Climbing stairs was correctly classiﬁed in 93% of the cases. We also achieved a good

correlation of the power calculations with the conventional stop watch measurements with a mean deviation of

2.35%. The system’s sensitivity to detect the transition towards frailty has been conﬁrmed. Furthermore, we

discussed the general suitability of the automated stair climb power algorithm in unsupervised, standardized

home-assessments.

1 INTRODUCTION

Functional ability is important for an independent liv-

ing but with age functional decline is inevitable. The

decline can be slowed down through timely preven-

tive measures. For this purpose, early detection of

performance changes is crucial. Usually, the func-

tional status is evaluated via assessments, covering

”strength”, ”mobility” and ”balance” as essential pa-

rameters for the functional performance (Hellmers

et al., 2017c). The power, which is related to the

muscle strength, has shown to hold an important role

in functional ability and seems to be a good indica-

tor for functional decline (Reid and Fielding, 2012).

For example, the power of the lower extremities is

measured by the Nottingham power rig, cycle ergom-

etry, rapid dynamic contractions on resistance train-

ing machines, maximal vertical jump (Hellmers et al.,

2017b), stair climbing, stair sprinting, or sit-to-stand

(STS) transfer (Zech et al., 2011). Bean et al. have

shown that the muscular strength correlates with the

determined stair climb power in the stair climb power

test (SCPT) (Bean et al., 2007) and timed stair tests

are considered as an objective measure of functional

abilities (Nightingale et al., 2014). Especially the

SCPT and the STS require less technique and physi-

cal demands. Therefore, they can be easily performed

in all age groups and are often possible even with a

beginning functional decline.

Figure 1 shows an assumed qualitative progress of

the functional ability over age. While the ability re-

mains stable until high age, at some point the ability

suddenly declines. Thus, frequent assessments (e.g.

monthly) would enable an early detection of func-

tional decline and an initiation of preventive measures

when they are needed most. It is important to detect

performance changes ∆a as soon as possible and to

start interventions at an early stage because they can

slow the functional decline and extend the time ∆t of

independent living. But since guided assessments re-

quire a lot of effort by health professionals (and thus,

hold a signiﬁcant ﬁnancial burden to healthcare sys-

tems), they can only be conducted on an occasional

basis.

Hellmers, S., Kromke, T., Dasenbrock, L., Heinks, A., Bauer, J., Hein, A. and Fudickar, S.

Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic Environments.

DOI: 10.5220/0006543900390047

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 39-47

ISBN: 978-989-758-281-3

Figure 1: Qualitative progress of functional ability over

time. Regularly performed assessments (lines on the hori-

zontal axis) enable the possibility to detect early changes in

the ability ∆a to start interventions and slow the functional

decline and extend the time ∆t of independent living.

A promising approach to overcome conventional

time-intensive and high-effort supervised assess-

ments are technology-supported unsupervised and

self-guided home-assessments, where the evaluation

takes place during daily life activities. Frenken et al.

(Frenken et al., 2012) pointed out that there is a clear

need for technical support for implementing mobility

assessments unsupervised in domestic environments

in order to objectively measure capacity and perfor-

mance of patients. Due to the regular and routinized

manner of the SCPT execution, climbing stairs is a

well suited activity to be considered for unsupervised

assessments in daily living.

The SCPT is suitable for home-assessments, as long

as elderlies climb stairs in their daily living. Whereby,

wearable technologies offer a good approach for un-

obtrusive measurements.

Thus, we investigate an automated assessment of the

(muscular) strength based on the common SCPT via

3D-accelerometers and 3D gyroscopes - so called

inertial measurement units (IMUs)- integrated into

belts. For an automated evaluation of the stair climb-

ing performance two steps are necessary:

1. Classiﬁcation of phases of stair climbing,

2. Calculation of the stair climb power.

The rest of the paper is structured as follows: We de-

scribe related work in Section 2 and the study design

in Section 3. The method for the detection of stair

climbing is presented in Section 4 and for power cal-

culation in Section 5. The results and the evaluation

are described in Section 6 and we discuss the suitabil-

ity for home-assessments in Section 7. Finally, we

describe our conclusions and future work in Section

2 STATE OF THE ART

Regarding the suitability to classify climbing stairs,

various combinations and placements of IMU-sensors

have been shown to be practical. Table 1 shows a se-

lection of studies and the applied sensors and posi-

tioning as well as the study size and the age of the

participants. It is to mention that these studies do not

only concentrate on as- or descending stairs but on the

general recognition of several activities such as walk-

ing, sitting, standing, lying, running or cycling.

The ﬁrst four examples show the inﬂuence of the sen-

sor placements on the accuracy: Zheng et al. (Zheng

et al., 2014) applied three IMUs positioned at one

thigh, shank and foot and pressure sensors at the

feet to achieve an accuracy of 99.03% with a lin-

ear discriminant analysis (LDA) classiﬁer. Khan et

al. (Khan et al., 2010) used one accelerometer at

the chest and artiﬁcial neural nets (ANNs) as well as

autoregressive (AR) modeling to get an accuracy of

99.0% in stair climbing recognition. Fida et al. (Fida

et al., 2015) achieved an accuracy of 97.2% with a

Support Vector Machine (SVM) as classiﬁer and a 3D

accelerometer and gyroscope attached to the shank of

the dominant leg.

In the other studies mentioned in Table 1 the sensor

was positioned at the waist and therefore have a sim-

ilar sensor attachment as in our study. These studies

show a lower precision, but the placement at the waist

is less obtrusively and suitable for unsupervised at-

tachments by the participants.

Shoaib et al. (Shoaib et al., 2014) applied an IMU

and achieved an accuracy of >95% with a K Nearest

Neighbor (kNN) classiﬁer, whereas Fareed (Fareed,

2015) achieved 93.8% with a similar setting. A Sup-

port Vector Machine (SVM) as classiﬁcation method

was applied by Sun et al. (Sun et al., 2010) and the

accuracy was 93.8 %. The selection shows that the

used classiﬁcation method also varies within differ-

ent studies. It has been shown, that the type of clas-

siﬁer affects the accuracies, but the accuracies also

vary within one method due to the inﬂuences of dif-

ferent data sets and their complexity. Besides the sen-

sor placement and the applied classiﬁer, the derived

features and the speciﬁcations of the sliding window

are important parameters. Therefore, we investigate

and describe these parameters in Section 4.

It should be pointed out that the mentioned studies

have been conducted with rather young participants,

which clearly does not represent the intended primary

user group to beneﬁt from such systems. Thus, only a

small variety of stair climbing patterns might be cov-

ered in the discussed works. For this reason, research

for recognition of stair ascending in larger studies and

HEALTHINF 2018 - 11th International Conference on Health Informatics

Table 1: Studies investigating classiﬁcation of stair climbing via machine learning. The type of applied sensors (accelerom-

eter (acc.), gyroscope (gyro.), magnetometer (magn), barometer (baro) and sensor positioning, as well as the sample size

and the age of the study population are listed. The classiﬁcation method with the best accuracy is written in bold. The ab-

breviations of the methods are: Linear Discriminant Analysis (LDA), Artiﬁcial Neural Nets (ANNs), Autoregressive (AR)

Modeling, Decision Trees (DT), Bayesian Networks (BN), Naive Bayes (NB), Support Vector Machine (SVM), Multiclass-

(Hardware Friendly)-SVM (MC-(HF-)SVM), K Nearest Neighbor (kNN), Rule-Based Classiﬁers (RBC), Logistic Regression

(LR), Static Classiﬁer (SC), Hidden Markov Model (HMM).

Precision Sensor Position Classiﬁcation Method size age [years] Reference

99.03% 3D acc & gyro thigh, foot, LDA 5 24.8(±1.3) (Zheng et al., 2014)

& magn; pressure shank

99.0% 3D acc chest ANN, AR 6 27 (mean) (Khan et al., 2010)

97.2% 3D acc & gyro shank SVM 9 29(± 5) (Fida et al., 2015)

> 95.0 % 3D acc & gyro waist SVM, kNN, RBC, 10 25-30 (Shoaib et al., 2014)

LR, DT, BN, NB,

93.8 % 3D acc waist SVM 7 25-46 (Sun et al., 2010)

93.2 % 3D acc & gyro waist DT, NB, kNN, SVM N/A N/A (Fareed, 2015)

87.2 % 3D acc waist MC-SVM, 30 19-48 (Anguita et al., 2012)

MC-HF-SVM

84.6 % acc, baro, ... waist SC, HMM 12 20-30 (Lester et al., 2006)

especially for older adults is important because their

movements can deviate from movements of younger

adults. For example, Stacoff et al. (Stacoff et al.,

2005) found in their study that younger participants

walked faster and produced larger vertical ground re-

action force (GRF) maxima during level walking and

on stair climb than the older age group. Considering

this point, we carried out a larger study, which is de-

scribed in the next section.

After concentrating on the stair climbing recognition,

we now focus on the power-calculations. According

to Bean et al. (Bean et al., 2002), power is a physi-

ological attribute related to strength and reﬂects the

ability to perform muscular work per unit of time.

Power P can be calculated by the following equation

P = Fv = mgv = mg



kg · m

= W



, (1)

where F is the force, v the velocity, m the par-

ticipant’s weight, g the gravity, h the covered height

and t the test duration. Usually, the time for the

SCPT is measured by medical professionals via stop-

watches. While various studies concentrate on stair

climb recognition, they have not yet conducted such

IMU based power calculations. Regarding Equation 1

we need to determine the parameters ”stair climbing

duration” and ”covered height” in the detected phases

of stair climbing.

3 STUDY DESIGN

In order to develop an inertial-based system to mea-

sure the stair climb power test and to evaluate its

sensitivity and speciﬁcity, we conducted the follow-

ing laboratory study. IMUs integrated into belts

were used due to their easy applicability, ﬂexibility

and suitability for measuring daily life activities. In

this study, the SCPT was measured via conventional

manner assessments with manual stopwatch measure-

ments (assumed as gold standard) and IMU-based

sensor belt recordings. Overall, 83 participants aged

70-87 years (75.64± 4.17 years) performed the SCPT

twice.

Initially, the examiner stands with each participant at

the base of the stairway with eleven steps. The partic-

ipants were instructed to safely climb the stair as fast

as they could and to stop on the 10th step. In accor-

dance with the proceedings introduced by Bean et al.

(Bean et al., 2007), participants were allowed to use

the handrail if necessary.

Figure 3 shows the used stairway with eleven steps.

The yellow footprints mark the start positions,

whereby the participants can choose their preferred

side. The ﬁrst yellow line is for safety issues and the

reduction of the risk of stumbling. The participants

should stop at the second line because the SCPT is

usually performed on 10 steps. The red boxes mark

the light barriers.

Besides the SCPT, other geriatric tests such as the

Short Physical Performance Battery (SPPB), Frailty

Criteria, de Mortan Mobility Index (DEMMI), 6

Minute Walk Test (6MWT), and Counter Move-

Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic

Environments

Figure 2: Process of activity recognition and stair climb power calculation. After data collection, data pre-processing follows.

Before the classiﬁcation of activities, feature sets must be selected. After classiﬁcation of the activity of ascending stairs, the

power can be calculated.

Figure 3: The stairway used in the study: The participants

start at the yellow footprints and should stop before the sec-

ond yellow line at the 10th step. The red boxes mark the

light barriers. The coordinate orientation of the sensor belt

is illustrated: x-direction is sidewards; y-direction is up-

wards; z-direction is straight forward.

ment Jumps (CMJ) were performed. After these

assessments, the participants wear the sensor belts

continuously for one week in their daily life. The

study and the utilized technologies for each assess-

ment item are summarized in (Hellmers et al., 2017a).

Sensor Belt

The measurement unit integrated into the belt in-

cludes four sensor types: A Bosch BMA180 triaxial

accelerometer, which measures the acceleration force

in g ≈ 9.81ms

−2

applied to the device on all three

physical axes (x, y, and z). The accelerometer has

the following parameters: Sensitivity ranges from

1G up to 16G and the chip supports sampling rates

up to 1200 Hz. The STMicroelectronics L3GD20H

gyroscope measures the device’s rate of rotation in

deg · s

−2

around each of the three physical axes (x,

y, and z). A magnetometer measures the ambient

geomagnetic ﬁeld for all three physical axes (x, y,

z) in µT and a barometer measures the air pressure

in hPa. The coordinate orientation of the sensors

is shown in Figure 3. A sampling rate of 100 Hz

is used for all four sensors in this study, since the

parameter settings have a signiﬁcant inﬂuence on the

recognition accuracies (Fudickar et al., 2012).

4 STAIR CLIMB DETECTION

Figure 2 shows the general processing work-ﬂow of

activity recognition via the intended machine learn-

ing approach and stair climb power calculation. Af-

ter data collection during the different geriatric tests

within the assessment (see section 3), steps of pre-

processing follow, as well as the feature extraction for

the classiﬁcation. The stair climb power is calculated

for the time spans, classiﬁed as stair ascending.

In order to describe our algorithm, we focus on the

extracted features, the sliding window and the used

classiﬁers in the following sections.

4.1 Derived Features

Deriving a minimal feature-set is an essential step

for machine-learning based classiﬁcation algorithms

in order to assure efﬁcient classiﬁcation. In order

to train the classiﬁer, data sets of 80% (n=66) of all

participants are considered. The activities of stand-

ing, walking, ascending and descending stairs were

selected from the whole assessment and pooled. Ac-

cording to frequently used features in literature (see

section 2), we derived the following features:

Mean• Variance•

Root Mean Square• Standard Deviation•

Median• Entropy•

Correlation

Coefﬁcient

• FFT Energy•

HEALTHINF 2018 - 11th International Conference on Health Informatics

These eight features are considered per axis of ac-

celerometer and gyroscope, resulting in an overall set

of 48 features. The data of the magnetometer was

excluded due to its high inﬂuence to environmental

noise. Including the magnetometer data would re-

sult in an over-ﬁt for this speciﬁc stairway. Figure

4 shows, as an example, the standard deviation (SD)

in comparison to the root mean square (RMS) in z-

direction of the activities ascending and descending

stairs, walking and standing. As expected, the SD

for a static activity is quite small. Furthermore, while

there is only a relatively low scattering of the walk-

ing activity, the values for ascending and descend-

ing stairs scatter signiﬁcantly broader. Another ex-

ample is shown in Figure 5: The distribution of the

FFT Energy of the gyroscope in x-direction and Root

Mean Square (RMS) of the accelerometer data in y-

direction. While the FFT energy for standing is about

zero (static activity), the FFT energy for ascending

stair lies in a higher range than walking and descend-

ing stairs. The RMS is also lower than for the other

both dynamic activities.

Figure 4: Scatterplot of Standard Deviation (SD) and

Root Mean Square (RMS) of the accelerometer data in z-

direction (straight forward). The activities of walking and

standing show low scatterings, while ascending and de-

scending stair scatter signiﬁcantly broader.

4.2 Sliding Window and Classiﬁer

Besides the features, the sliding window and the used

classiﬁer are also crucial factors. Since Shoaib et al.

(Shoaib et al., 2014) have shown that an overlap of

50% of the sliding windows produces reasonable re-

sults, we considered a sliding window approach with

a 50% overlap.

In accordance with the related work, we used the fol-

lowing classiﬁers, due to their high sensitivity. All of

Figure 5: Scatterplot of FFT Energy of the gyroscope in

x-direction (sidewards) and Root Mean Square (RMS) of

the accelerometer data in y-direction (upwards). The mean

ranges of values for each activity are marked in the graph.

them are available in Mathworks’ MATLAB (version

R2015a):

• Decision tree (complex, medium, simple),

• Support vector machine (linear, quadratic, cubic,

ﬁne Gaussian, medium Gaussian, coarse Gaus-

sian),

• K-Nearest-Neighbor (ﬁne, medium, coarse, co-

sine, cubic, weighted).

The F1-Score was used for the evaluation to consider

both precision and recall. Figure 6 shows the F1-

Scores for the recognition of the activity ”climbing

stairs” for each classiﬁer of the three used methods

with the best result for different window sizes. Table

2 summarizes the results.

Table 2: Best results of F1-Scores by optimized window

sizes and an overlap of 50% of the sliding windows for the

three used classiﬁers.

Classiﬁer Window Size [s] F1 Score

Decision Tree 1.9 81.50

SVM 1.7 93.00

k-NN 1.6 93.99

While decision trees show the worst performance

with an F1-Score of 81.5, k-NN (93.99) and SVM

(93.0) achieve similar results. The best window size

for the k-NN classiﬁer is 1.6s and for the SVM clas-

siﬁer about 1.7s. At the basis of this result, in the

following, we concentrate on the k-NN classiﬁer.

Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic

Environments

Figure 6: F1-Score for three different classiﬁers complex

tree, Cubic SVW and Fine k-NN in correlation to the win-

dow size.

4.3 Post-Filtering

To remove incorrectly detected stair climbing activi-

ties, a minimum duration of 3s for this activity was

deﬁned and shorter durations declared as ascending

stair were ignored in a post-ﬁltering step. This thresh-

old value is based on stopwatch measurements over

all participants, whose needed duration was always

> 3s. The majority of studies use a stairway of 10

to 12 stairs for testing, which is likely to be the av-

erage ﬂight length available in buildings and there-

fore a practical length for testing (Nightingale et al.,

2014). But in those studies, which are looking at med-

ical conditions involving the heart and lungs, longer

stairways are used to elicit a more cardiovascular re-

sponse. Therefore, stair ascending activities with less

stairs than 10 are less meaningful for the SCPT.

5 CALCULATION OF STAIR

CLIMB POWER

In order to calculate the power and in accordance with

Equation 1, the covered height and the duration for

the SCPT are measured. While the duration is de-

termined by the sequence of the recognized activity,

the height was evaluated by counting the steps within

this sequence of ascending stairs. Figure 7 shows, as

an example, the acceleration of a sequence of climb-

ing 10 steps. The activity of climbing stairs or other

rhythmic activities usually show repetitive patterns.

In cases of walking or ascending stairs, the impact

of the foot on the ﬂoor causes a peak in acceleration

data. These peaks were counted and assumed as steps.

On the basis of the step number n the covered height

Figure 7: Acceleration in x-,y-, and z-direction during a

phase of ascending stairs in a laboratory setting. The single

steps can be recognized by characteristic peaks.

h can be calculated by h = n ∗ 16.5, where 16.5 is the

height of one step in cm. In most European coun-

tries the height of steps is standardized by building

regulations. Barometers can also be used for eleva-

tion measurements. But due to the low accuracy of

the barometer in our study (±10 cm), we decided to

use the peak detection algorithm based on the accel-

eration data for height estimations.

The participant’s weight was measured via a sta-

diometer (seca 285) and the gravity is assumed to be

g = 9.81ms

−2

6 EVALUATION

6.1 Recognition of Stair Ascent

Since the data sets of 80% (n=66) are considered for

model training, the other 20% (n=17) of data sets are

used for testing. Figure 8 shows the confusion ma-

trix of our Fine k-NN classiﬁer, with a window size

of 1.6s and an overlap of 50%. The recognition of

standing and walking show very good results with a

rate up to > 98%. Climbing stairs is correctly rec-

ognized in 93% of the cases’. Descending stairs was

incorrectly assigned as climbing stairs in 6% and as

walking in 10%.

6.2 Calculation of Stair Climb Power

Table 3 shows the mean deviations of power, test du-

ration and covered height of the sensor-based mea-

surements with the already presented k-NN classi-

ﬁer to stop watch measurements. The weight and the

gravity values are same in both calculations and there-

fore not further considered.

HEALTHINF 2018 - 11th International Conference on Health Informatics

Figure 8: Confusion matrix of activity recognition with a

k-NN classiﬁer.

Table 3: Mean deviations of sensor-based power measure-

ments to stopwatch measurements.

Mean Deviation [%] Median [%]

Power 2.35 4.71

Duration 14.49 6.46

Height 15.00 15.00

While the k-NN classiﬁer achieves quite good

power values with a mean deviation of 2.35 % to the

gold standard, the deviation of the estimated height

and durations are with respectively ≤15% very high.

As already described in Section 3 the duration of

climbing ten stairs will be measured by the stop watch

in the SCPT and is assumed as gold standard. But the

technically detected number of stairs is not exactly 10

in all cases. In our assessments, the number of passed

steps varied among users between 10 and 11 stair due

to the following reasons: The setting consists of an 11

step stair ﬂight, but some participants forget to stop at

the 10th step, as instructed. In addition, a step might

be not recognized due to the transition from standing

to climbing stairs. However, this isn’t a problem due

to the fact that according to Equation 1 the ratio of

height and duration inﬂuences the power (v = h/t).

Thus, the difference in the power of the gold standard

measurement (ten stairs) is small, due to the low effect

on the participants’ fatigue or the ﬂuency of the test

sequence (acceleration and deceleration) of climbing

one additional or less stair. But of course, the accurate

detection of the beginning and end of the activity is a

major task and needs further investigations.

6.3 Medical Sensitivity

In order to clarify the medical sensitivity of our sys-

tem, we compared its error to the medical required

sensitivity to detect the transition to functional decline

(as covered by frail state). Table 4 summarizes the

power values of our participants at baseline (t0) and

after 6 month (t1). They were categorized in groups

of frail and non-frail according to the classiﬁcation

of the Frailty Criteria. The mean deviation in power

between these groups is about 14% and therefore, sig-

niﬁcantly higher than our system’s deviation from the

gold standard measurements of about 2%. Thus, we

conclude that our system’s sensitivity is sufﬁcient for

medical meaningful detection of transitions towards

the frail state under controlled conditions.

Table 4: Stair climb power (P) of participants in our study

at baseline (t0) and after 6 month (t1) categorized in groups

of frail and non-frail according to the classiﬁcation of the

Frailty Criteria.

number P

[W] P

[W]

non-frail 56 2302 2298.0

frail 27 1979.5 1968.1

∆ 322.5 (14.0%) 329.9 (14.0%)

7 SUITABILITY FOR

HOME-ASSESSMENTS

We have introduced a ﬁrst approach to measure the

stair climb power via a single inertial sensor worn at

the waist. Due to the easy applicability, elderlies can

wear the sensor belt correctly without further assis-

tance. The participants of our study have worn the

sensor belt continuously without supervision during

their daily living for one week following their assess-

ments and written an activity diary for the week. In-

vestigating these data sets, we want to study the appli-

cability of our SCPT system to an unsupervised use

by detecting the correctness of detected stairs.

Figure 9 shows the acceleration data during climbing

stairs of one participant at home. This sequence was

classiﬁed as stair ascent by our algorithm. To validate

this classiﬁcation we compared our results with the

participants diary (ground truth). The diary and the

classiﬁed activity match in this case.

In comparison to Figure 7 the pattern and ampli-

tudes are signiﬁcantly different from the acceleration

data measured during the assessment (test situation)

although it shows the activity of the same partici-

pant. Thus, we could conﬁrm, that phases of climb-

ing stairs could also be recognized during these home-

assessments.

Furthermore, to clarify the degree of variations in

stair-climbing patterns for different environments by

investigating it in the participant’s daily life. There-

fore, the one-week measurements of the sensors will

be analyzed concerning the frequency, the covered

Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic

Environments

Figure 9: Acceleration in x-,y-, and z-direction during a

phase of climbing stairs in daily life.

steps and the estimated stair climb power of the ac-

tivity stair climbing. Regarding the medical mean-

ingfulness of unsupervised power measurements at

home, a comparison of the results in the laboratory

setting with results at home will follow. Within, es-

pecially the inﬂuence of diverging conditions such as

step number or the absence of a test situation will be

examined. For further validation of our stair climbing

detection in domestic environments, it is planned to

install ambient sensors at the stairways in the homes

of a subgroup of our participants.

8 CONCLUSION

Due to the importance of strength analysis to detect

functional decline in an early stage, we introduce stair

climb power measurements based on IMUs in a lab-

oratory setting. Therefore, we recognized the activity

of climbing stairs automatically via machine learning

and calculated the power based on the needed time

and covered height. Climbing stairs is correctly clas-

siﬁed in 93% of the cases. For power calculations, we

achieved good results in comparison to conventional

measurements with a mean deviation of 2.35%. The

system’s sensitivity to detect the transition towards

frailty has been conﬁrmed.

Additionally, we showed the general suitability of

sensor belt measurements at home and conﬁrmed, that

phases of climbing stairs could also be recognized

during home-assessments However, further investiga-

tions of the stair usage behavior of our participants

and the recognition of stair ascending in domestic en-

vironments are planned based on one-week measure-

ments at home following the assessments. The use of

ambient sensors such as RFID technologies or light

barriers in the homes of a subgroup of our participants

is intended for further validations of the activity de-

tection and the determined duration of the stair climb-

ing activity. Especially regarding the medical rele-

vance of unsupervised home-assessments, further in-

vestigations and comparisons between laboratory and

home results are needed.

ACKNOWLEDGEMENTS

The study is funded by the German Federal

Ministry of Education and Research (Project No.

01EL1422D). The study has been approved by the

appropriate ethics committee (ethical vote: Hannover

Medical School No. 6948) and conducted in accor-

dance with the Declaration of Helsinki.

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