Adapting Magnetic Resonant Coupling Based Relative Positioning Technology for
Wearable Activitiy Recogniton
Gerald Pirkl, Karl Stockinger, Kai Kunze, Paul Lukowicz
Embedded Systems Lab, University of Passau, Germany
www.wearable-computing.org

Abstract

ponents (price of all components 20 Euro for the transmitterand 15 Euro for the receiver) consumes a reasonable amountof power (17mA for the transmitter and 40mA for the receiver), and are easily wearable (the main size constraint are
the coils which are around 25mm3 ).
Our work is based on the observation that the requirements the system must fulfill to be a useful tool for activity recognition are very different from the requirements forwhich stationary magnetic systems have been built. Stationary
magnetic systems target sub centimeter precision, high speed
tracking over ranges of up to 3 meter. On the other hand, foractivity recognition, the system merely needs some sort of relative position related signal over short distances (50 to 80cm).
The signals do not have to be the actual position and orientation values expressed in any standard units. They just need to
be deterministic and reproducible.

We demonstrate how modulated magnetic field technologythat is well established in high precision, stationary motion
tracking systems can be adapted to wearable activity recognition. To this end we describe the design and implementation of a cheap (components cost about 20 Euro for the transmitter and 15 Euro for the receiver), low power (17mA for
the transmitter and 40mA for the receiver), and easily wearable (the main size constraint are the coils which are about
25mm3 ) system for tracking the relative position and orientation of body parts. We evaluate our system on two recognition
tasks. On a set of 6 subtle nutrition related gestures it achieves
99.25% recognition rate compared to 94.1% for a XSens inertial device ( operated calibrated, euler angle mode). On therecognition of 8 Tai Chi moves it reaches 94 % compared to
86% of an accelerometer. Combining our sensor with the accelerometer leads to 100% correct recognition (as comparedto 90% when combining the accelerometer with a gyro).

1Paper Organization The paper first describes the physicalprinciple behind the system and outlines and justifies the assumptions behind it. It then provides a detailed descriptionof the implemented system including the discussion of different design alternatives and problems. We finish with a threestep evaluation of the system. First we conduct an analysisof the signals produced by our system. Second we compare
the performance on a simple gesture classification task against
the Xsens MT9 inertial tracking sensors. There our systems
gets 99.25% recognition rate compared to 94.1% for a single
MT9 and 97,28 for a system of 3 MT9 sensors. Finally wedemonstrate the benefit of combining our sensor with an accelerometer on a more complex activity recognition task: theclassification of Tai Chi moves. Starting with a recognition
rate of 86% for the accelerometer, and 94% for our system
the combination of the two leads to 100% correct recognition(accelerometer/gyro combination 90%).

Introduction

Posture and motion of body parts is well known to be a key
component of many human activities. Thus, a significant proportion of research and activity recognition is based on posture and motion information. However, technology for capturing body motion and posture (often referred to as motion
tracking systems) is still far from being perfect. A much cited
overview of such technologies ( [9]) is titled ”No silver bullet
but an respectable arsenal”. From wearble application pointof view the problem is that many of the best technologies in
the ’respectable arsenal’ require stationary, often bulky infrastructure and are not suitable for wearable use. Today, the coreof wearable activity work still relies on accelerometers which
capture only a small part of posture and motion information(see Related Work below).

Related Work The use of accelerometers for motion based
activity recognition is motivated by the availability of cheap,
low power, easy to handle sensors. The disadvantage of accelerometers is that they are able to capture only a smallpart of motion and posture information. On the vertical axis
they mix the gravity component (=vertical orientation) with’true’ acceleration. On the horizontal axis they capture only

General Idea Existing magnetic field systems (e.g. the”flock of birds” system from ascension: www.ascensiontech.com) require a bulky, power consuming stationary transmitter and cost thousands of dollars. In our system both thetransmitter and the receiver consist of a couple of cheap com1

speed changes, but no orientation information. Other common
sensors are gyroscopes and magnetic field sensors. The former capture rotational motions but give no information abouttranslation and orientation. The later capture the absolute orientation with respect to the earth magnetic field. The mostadvanced wearable motion tracking technology are so called
MARG (Magnetic Rotation Gravity e.g. [2]) system that combine 3D acceleration, 3D gyroscope and 3D earth-magnetic
field with appropriate algorithms to deliver the absolute orientation of the device. Different commercial MARG systems
exist (e.g. Xsens www.xsens.com) and cost in the range of1000 Euro per unit. They work well for many application
however they are sensitive to disturbance caused by magnetic
fields (e.g. caused by 50Hz mains currents). Also they provide no direct relative position information. Instead the information must be derived from several sensors on adjoining
body parts through trigonometric calculations. Another technology that gives information about the relative orientation ofbody parts are elongation or bend sensors integrated in theuser’s garment (e.g. [7]). It has also been proposed to use
capacitive sensors to measure joint angles [1]. A stationary
magnetic tracking systems has been used in a large backpack
for a wearable application by [5]. A magnetic tags based interface for music has been proposed in [8].
In previous work [3] our group has already demonstrated
a simple onbody measurement using the magnetic resonant
coupling principle (measurement of elbow angle). However
this was done with a 1D system ignoring orientation. We also
did no evaluation on a recognition task. Finally, the system
was a simple first prototype with none of the hardware optimizations described in the paper.
Paper Contributions As describe above the general principle on which this work is based is not new and even has
been initially demonstrated for simple on body measurements
before. The contribution of the paper is to demonstrate that(and how) a system based on this principle can be built that issuitable for wearable use and useful for activity recognition.
By useful we mean that the system leads to better recognition rates then comparable wearable sensors (accelerometers
or MARG), which we demonstrate empirically. While thesystem is simple in terms of the numbers of components, it
involves a number of non trivial design issues. These are discussed and the resulting solution described.

2
2.1

consequence the oscillating magnetic field induces a voltage
in the receiver coil. The magnitude of this voltage depends on
the relative orientation of the sender and receiver coils and the
distance between them. Thus, it can be used in principle as arelative positioning system.
Compared with other positioning systems resonant magnetic
coupling has a number of interesting properties:
1. The attenuation of an oscillating magnetic field is reasonably independent of the environment. The main loss
mechanisms are so called ’eddy currents’. These are circular currents induced in conductors exposed to changing magnetic field. The amount of energy loss depends
among others on the volume of the material so that small
volume metallic objects cause only little disturbance.
There is no need for line of sight between receiver andthe sender. Human body causes virtually no disturbance.
2. Since the receiver is resonant circuit tuned toa narrow frequency environmental,
magnetic
fields (e.g. caused by the AC current of household
installation) and stationary magnets do not disturb it.
This is in contrast to sensors utilizing the earth magnetic
field (like most MARG trackers) that are highly sensitive
to the described disturbances.
3. Unlike a radio frequency (RF) based system, a magnetic
resonant coupling system does not radiate energy. In oursystem there is no antenna and thus no RF transmission 1 .
The magnetic field built up by the coils is fully ’retracted’as the energy flows back into the electric field of the capacitor during the next oscillation cycle. Apart from thedamping losses, the only energy that is extracted fromthe system is the energy needed to induce the voltage inthe receiver’s coil and any energy lost due to eddy currents as described above. Thus relatively strong signals
can be generated without excessive cost in terms of energy consumption.
4. The energy in the magnetic field attenuates with distancer as 1/r6 . At first this appears to be a disadvantage, as
the range of the system is obviously limited. However,
we need to remember that for on body applications we
hardly need ranges of more than 1m (a sender placedin the body center) and in many cases around 50cm isenough. At the same time the strong dependence on distance means that other disturbances have less effect on
the measurement result. There are also no problems withreflections and multi-path propagation.
In summary resonant magnetic coupling is an attractive, robust technology for on body relative positioning.

System Concept
Physical Principle

The principle behind our sensor is that of ’resonant magnetic coupling’. The sender generates a magnetic field that
oscillates with certain well defined, narrow frequency. Thiscan be achieved by driving an appropriate coil/capacitor combination with a sinusoidal current of the desired frequency.
This field carries energy which falls with rising distance. The
receiver contains an LC oscillator circuit (in essence a coiland capacitor) tuned to precisely the same frequency. As a

2.2

Relative Positioning Principle

The down side of resonant magnetic coupling is the complex relation between signal strength and the relative position
1 In reality cables and data lines act as parasitic antennas and some energy
is indeed radiated.

2the field means that for a given distance and fixed orientationof the receiver with respect to the field lines the induced voltage depends on the location of the receiver around the sender(see coils a and c). The field is symmetric with respect to rotation around the axis parallel to the coil which is the z-axis of
the sender coordinate system in Figure 1(a). Thus the location
around the coil is determined by the angle θ in our coordinate
system.
In summary the intensity of the voltage induced at the receiver depends on r, φ, α, β. An important thing to note is thatthe dependence is subject to symmetries with respect to 180
degree rotations in the x-y plane. Thus, for example, the fieldstrength in coil a in figure 1(b) will not change if it is moved
horizontally to the other side of the sender coil. This meansthat the relative position measurement is likely to contain ambiguities with respect to such 180 degree rotations.

Receiver

c)

d)

z

r
b)

a)

φ θ
x
y

Sender(a)(b)

Figure 1. (a) Orientation and Distance overview.
(b) Schematic Magnetic field with receiver coils.

Resulting Sensing System From the above it is clear that asystem with a single sender and receiver coil will not be able
to yield all six degrees of freedom tha define the spatial relationship between the sender and the receiver. The most obvious solution is to use three perpendicular sender and receivercoils. The sender has to pulse the three coils sequentially.
Each pulse induces voltage in all three receiver coils. Thus
single measurement results in a 3x3 matrix of signal strength
values: three receiver coil values for every one of the three
sender coil pulses.

xx yx zx
Mi = xy yy zy (1)
xz yz zzand orientation of the sender and the receiver (another is thelimited range which, however, is not an issue for on body systems). Thus, the main design issue is the ability to deal withsuch complex dependencies. To understand how this can beachieved we first define the degrees of freedom which we consider for the relative orientation and position measurement.
We then describe how the intensity of the signal at the receiver depends on these degrees of freedom. Based on this we
describe the design of a relative positioning system.
Coordinate System and Degrees of Freedom The coordinate system on which discussion is based is shown in Figure1(a). We assume the sender to be at the origin with a coiloriented parallel to the z axis. The location of the receiver inthis coordinate system can be given spherical coordinates by adistance r from the origin and two angles θ and φ. In addition
the receiver has its own local coordinates system. Again we
assume a coil to be oriented along the z-axis. The orientationof the receiver with respect of the sender is the given by 3 angles α, β, γ that describe the rotation of the local coordinate
system with respect to the global one.

In the above equation the suffixes refer to the sender axis thatwas pulsed and the ’normal’ letters to the receiver axis onwhich the field strength was measured. Thus xy denotes thestrengths of the voltage induced in the x coil of the receiver by
pulsing the y coil of the sender. The elements of the measurement matrix can in principle be used to derive all six degrees
of freedom of the relative position.

Position and Orientation Dependence Given a specificcoil and a fixed oscillation frequency the voltage induced in
the coil is related to the number of magnetic field lines passing
through the coil. Figure 1(b) shows the field line structure of atypical coil and illustrates the sources of signal strength variation. First, the density of the magnetic field lines decreases
with increasing distance. Thus, everything else being identical, the voltage in a further away coil will be smaller (coil bcompared to coil a). This is the 1/r6 dependence. Second,rotating a receiver coil in relation to the field lines reduces theeffective area through which the field lines can pass and withit the induced voltage. Thus the highest voltage is induced ina coil parallel to the field lines while a coil perpendicular to
the field lines has zero voltage (see coil d). Note, that rotations around the axis passing through the coil are not relevant.
In our coordinate systems these are rotations around the z-axisof the local coordinate system (γ). Finally, the heterogenity of

Implementation Consideration In general there is no
closed analytical expression that describes the relation between M and those degrees of freedom. In real systems thefield structure is also likely to significantly deviate from theideal shown in Figure 1(b). It is usually determined using
complex numerical simulations. The results of such simulations can be used to build lookup tables or piecewise approximations for the relative position computations. In addition,
the field would in general be ’shaped’ through appropriate coil
design can make the computation easier and more exact.
Beyond such fundamental considerations there are a number of practical issues that make exact reliable distance measurement difficult. For one the 1/r6 attenuation means thatthe system must have a huge dynamic range. While the signals for small distances are in the range of a Volt, the largest
distance will results in sub µV values. The need to amplify
3such low voltage means that the system must be very carefully
designed to avoid picking up noise from the environment. The
stronger the signals that one tries to generate the more difficult it is to keep a clean signal shape. There are also a number
of issues involved in the coils selection as the size of the coil
is related to inductance which in turn determines the capacitor
needed to achieve certain eigenfrequency. Finally the need topulse the sender coils can lead to crosstalk with inactive coils.
It also require fast switching times between the amplification
circuits to allow fast sampling rates.
Overall it can be said that building a sender/receiver pairthat leads to some sort of relative position related voltage being induced in the receiver over short distances is easy. Turning such a pair into an exact distance and orientation measurement system working over larger distances is an entirely
different type of problem, which explains the size, price andcomplexity of existing, stationary systems.

2.3

without actually providing a measurement in any standard unit
(see section 3.3).
Accuracy The most common sensor location in wearable
activity recognition applications are wrist, torso, arms and upper and lower legs. For routine activities none of the above
is moved with sub centimeter accuracies. In fact, for many
activities, trajectories will vary by up to a few centimeters between individual executions. As an example consider bringing a bottle to your mouth for drinking and a wrist mounted
sensors. Clearly, there will be applications (e.g. in high performance sports) where accuracy within a few centimeters is
not enough. Our system does not target such applications.

3

System Implementation

The system consists of a single three coil sender and an
arbitrary number of receivers (each also containing three perpendicular coils). As described in the previous section thesender sequentially pulses each coil and the receivers register
the signal intensity on each of its coils.

System Concept

The basic idea behind this work is that for a sensor to bea useful activity recognition tool it does not have to be much
more then a ”sender/receiver pair that leads to some sort ofrelative position related voltage being induced in the receiver
over short distances”. The main considerations that lead tothis assumption can be summarized as follows:

3.1

Sender

The sender holds three orthogonally oriented coils and asignal generator circuit. To avoid interferences and magnetic
coupling the coils are split up and spatially separated. The
sender amplification board uses a square shaped input signal
and transforms it into the 10 mA sine shaped output current
applied to the coils. The sender sequentially puts the output signal on the emitter coils for 2.5 ms. As described in
the previous section sequential pulses on the 3 sender axes
are needed to deal with orientation dependence of the signal
strengths.
In an initial version of the sender we used a quartz controlled signal generator to generate a 32 kHz sine shaped output current. Experimental evaluation has shown that high inductivity of the emitter coils lead to small range at this frequency. The maximum range was between 30 and 45 cm depending on the used transmitter coils.
To overcome this problem we have move to the 20 kHz frequency. A microcontroller sequentially puts the square shaped
signal on the input channel of the x,y,z axis of the sender for
2.5ms. To synchronize the receivers, the sender broadcasts a
synch packet over an RF channel. Initially we tried to synchronise the receivers by using a synchronisation break after
the x, y, z sequence. Unfortunately if the distance between
the sender and the receiver is too high the signal-to-noise ratio made this type of synchronisation unreliable.

Range Many activities are determined by hand actions performed somewhere in front of the body. With an average
male arm span of about 170cm (arms spread from fingertip
to fingertip) for most such activities we can assume a distance
between chest and the wrist (where the sender and receiverwould likely be placed) to be in the range of 50 to 80cm. In
many applications (e.g. nutrition monitoring) the hand comes
into the immediate proximity of certain body parts, so thateven smaller ranges (20 to 30 cm) would be sufficient. For leg
related applications we may fix the sender on the belt and thereceivers somewhere below the knee so that 50 to 80 cm range
would also be enough.
System Output The fact that an activity is determined by
the relative position and orientation of two body parts does
not mean that the recognition system must know the actual
distance and angle values in any standard units. In fact, inmany systems the distances would be combined with othersignals to define a lower dimensional, abstract feature space.
What is needed is a signal that is (1) different for different relative positions and orientations and (2) reproducibly the samefor the same positions and orientations. Thus in principle theelements of the measurement matrix M could be used as input to the recognition system. However it is well known thattraining a classifier on such ’raw data’ that has a complex relation to the factors that actually separate the classes requiresa large amount of training data and often produces poor results. As a consequence we define a set of values that are aclear indication of the magnitude of the 6 degrees of freedom,

3.2

Receiver

Each receiver measures the magnetic field strength using
three orthogonally orientated coils (Picture 2). Dependingon the orientation of the receiver and also depending on thesender side axis which is currently generating a magnetic
field three different voltage levels are induced at the three
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The induced voltage is filtered to remove the induced parts
which do not lie in the intervall of [19500Hz; 20500Hz]. A
voltage rectifier transforms the ac voltage to a dc voltage. Depending on this input voltage level, the amplification circuit
boosts the rectivied input voltage. A hardware peakdetector
and hold mechanism stores the maximum voltage level. The
Microchip DSPic digitalises the attached voltage and deletes
the hold buffer. The microcontroller sequentially samples foreach sender axis all 3 receiver axes and transmits the data witha connected bluetooth module to the data recorder. Therefore
a complete measurement consits of 9 adc values. (Eq.1)!

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As described in section 2.3 as system output we do not use
the raw measurement matrix but values that are more intuitively connected to the individual degrees of freedom:1. The sum of all 9 elements of the matrix as indication of
the distance.
2. The ratios of signals on pairs of receiver coils from asingle sender coil (e.g. xyxx ) as indication of the angles
α, β, γ by which the receiver is turned.
3. The ratios of signals received by a single receiver coilwhen transmitting with different sender coils (e.g. xyyx )as indication of the angles θ, φ

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The paramters are based on the observation that when a decrease of the signal in one of the elements of M is caused
not by increase in distance but by a rotation, then it is always
accompanied an increase of a signal on another axis. If thex coil of the receiver is parallel to the field lines and receives
maximum signal then the y coil will be perpendicular and receives none. As we rotate the receiver around the z axis thex becomes gradually ’less parallel’ while the y coil becomes’more parallel’.

Initial Architecture Initially the receiver was based on asimple magnetic coil. After rectifying the signal, a logarithmic amplifier module rises the input signal for the receiver.
This architecture was fine when we were working with justone coil. However for a 3 sender 3 transmitter system it was
too slow due to the high saturation and falloff times of the receiver circuit which only led to a sampling rate of 50 Hz andlow range performance of 45 to 50 cm at 20 kHz.

4

Second Receiver Version The second receiver version uses
a different coil architecture: each axis consits of a pair of coilswith a center tap. This allows to use an operational amplifier
with voltage dependend back coupling. The induced voltage
is presented to a signal regeneration circuit which filters, rectifies and amplifies the input signal. The two step amplifier
circuit automatically adjusts the amplification multipliers using diodes with different threshold voltages. If the input voltage is high (as it is in near distance measurements), the diodes
reduce the amplification at the first and second amplifier tolimit the voltage level. If the sender and receiver are far apart,the diodes are non-conductive and the amplifiers both rise thevoltage.
After regeneration a hardware peak detector with holdmechanism stores the maximum voltage level. A transistor
allows to clean the hold mechanism after reading the voltage
level. We estimate the saturation and falloff times of this circuit which lead to a sampling rate of 106 Hz and a maximum
range of 65 cm.

Experimental Evaluation

We evaluate our system in three steps. First we look at the
distance and angle data to verify that the features defined in
3.3 display the desired qualitative correlation with distanceand the angles. We also investigate the influence of enviromental factors on signal quality. Second we compare theperformance on a simple gesture classification task against
the Xsens MARG sensors (euler angles on three body parts
derived from 3D acceleration, 3D gyroscope and 3D earthmagnetic). Finally we demonstrate the benefit of combining
our sensor with an accelerometer on a more complex activity
recognition task: the classification of Tai Chi moves.

4.1

Signal Level Evaluation

Relation between Distance and Signal We place the receiver at a fixed position (and orientation) and increase thedistance between the sender and the receiver in 2.5 cm steps
from 10cm to a maximum of 80 cm along one reception axis.
The orientations of both sensors are fixed.
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0◦ to 180◦ and compute the signal ratios on the two relevant
axes (see 2). Figure 3(c) presents the results.
Sensibility to Disturbances Having attached the sender tothe wrist and the receiver to the shoulder, we place different
objects between the magnetic measurement system. The distance between the receiver and the sender is 23 cm. We label
the measurements while the object is ”disturbing” the system.
Reference value is the mean value of the undisturbed measurements. Table 1 presents the objects which have been tested
how they influence the measurements.

After adding all 9 ADC values, we smooth the complete measurement set using a low pass filter and calculate the meansum for each reference point.
The results are shown in figure 3(a).
Between[10cm; 40cm] the system shows a high resolution which flattens with increased distance. The gradient especially in[10cm, 40cm] is adjustable by changing the hardware amplification multipliers. The signal to noise ratio in [65cm; 80cm]
is near one and therefore the maximum distance is about 65cm.
The deviations between the 9 value sum approximation andObjectArm
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metal plate

45

Estimated Distance in cm

40

35

30

25

Table 1. These objects have been tested how they influence20

the measurements.

15

10

∆s
1.3%
3.9%
−0.3%
5.0%
9.5%

15

25

35
Real Distance in cm

45

55

Conclusion The analysis shows the expected performance:
our values clearly depend on the respective degrees of freedom although they do not give an exact measures with for example the ratios that we use for angle estimation varying with
distance. Most important, the curves indicate that different
positions and orientations will in most cases produce different
values, as required for activity recognition.

Figure 4. Influence of angle on distance approximation
true distance illustrated in figure 4. The y-axis shows a piecwise linear approximation based computation of the distancetrained on the sum of the 9 values. To generate the figure wehave placed the receiver at 15,25,35,45,and 55 cm and rotatedit by 720◦ and compared the predictions of the piecwise linear
approximation to the true distance.

4.2

Relation between Receiver Angle and the Signal We use
a fixed sender position and orientation. The receiver is moved
along a circle always pointing towards the sender. We record
the adc values at discrete 10◦ steps around the receiver from0◦ to 180◦ compute the signal ratios on the two relevant axes.
The result is shown in Figure 3(b)Comparison to XSENS Systems

We compare the usefulness of our sensor for a simple gesture classification task to the Xsens MARG system. The
Xsens system is among the most advanced wearable motion
and posture tracking systems (cost around 1000 Euro per sensor) and is widely used in activity recognition research.
The selected set of gestures is motivated by our previous
work on nutrition monitoring. It contains taking a piece offood to the mouth (Fig. 5(a)), drinking from a cup (b) and
four gestures that are similar, yet nutrition unrelated: touching the chin (e), raking the nose (c), touching the earlap (d)Relation between Sender Angle and the Signal In this experiment the receiver is centrally positioned, the sender isplaced at the circle and moved along the circle around the receiver. As before, we record the adc values in 10◦ steps from6

and touching the forehead (f). All gestures involve a similar
hand posture and differ in the wrist placement by less than
10cm. Thus the recognition problem is non trivial and tests
assumption that although our system does not explicitly compute relative positions, it is able to separate subtle, spatial position related actions.

(a)

(b)

single Xsens on the upper arm, a combination of an Xsens on
the upper arm and the forearm, and all three Xsens is shownin the table below.
SensorRecognition rate
Magnetic sensor
99.29%
Xsens (all 3 sensors)
97.28%
Xsens (Forearm only)
94.1%
Xsens (Upper arm only)
93.7 %
Xsens (upper and lower arm) 96.9%
It can be seen that our sensors tracking the forearm outperforms a forearm mounted XSens by over 5% (99.29 % recognition rate to 94.1%). As expected all three XSens together
perform best of all the XSens based configuration. However
they are still 2% behind our sensor (97.28%).

(c)

4.3
(d)

(e)

(f)

Comparison to and Combination withAccelerometers

Our next experiment focuses on a comparison with an accelerometer and more important on the evaluation of the usefulness of combining our sensor with an accelerometer. The
investigation is motivated by the fact that in terms of sensor
system complexity and price our sensor is more comparablewith an accelerometer then with a 1000 Euro MARG unit.
In addition, accelerometers are the most widely used activity
recognition on body sensors and are increasing integrated
As case study we take the recognition Tai Chi gestures.
The choice is motivated by previous work ( [6]) that has
shown that Tai Chi and in general Kung Fu recognition using wearable motion sensors is feasible. At the same time wehave seen that recognition is non trivial and far from perfect.
Unlike the experiments in the previous section, the Tai Chi
case study targets not static gestures but motions.

Figure 5. Gestures to be classified.
Experimental Setup The magnetic receiver is placed at thechest of the test person, the magnetic emitter at the forearm.
The Xsens sensors are attached to the forearm, the upper arm
and the torso. The use of three Xsens sensors is motivated by
the fact, that the gestures are determined not by the wrist orientation alone, but by the relative position and orientation of
the wrist to the body. Such a relative position and orientationis the information that our sensor captures. The three Xsens
together capture the posture of the shoulder and the elbow
which determine the relative position of the wrist with respect
to the torso. During the recordings the test person keeps thearm at the same level. Each gesture is repeated 30 times. We
only record data while the hand is at the current wished position and we do not record the way to the position and back tothe start position of the hand.
Recognition Approach We train the Weka J48 Decision
tree algorithm on the following features:• Magnetic sensors: sum of all 9 adc values as rough disy
tance estimation and xxxy , yyz , zzxz as relative angle values, ij
is the adc value measured at receiver coil i while sendercoil j is emitting the magnetic field. All these magnetic
sensor values have been low passed filtered.
• XSens: Euler angles (roll,pitch and yaw) computed by
the sensor from the 3D accelerometer, gyroscope, andmagnetic field signals. The proprietary algorithm includes complex filtering to stabilize the signal.

Experimental Setup The magnetic field sender is mounted
on the wrist, the receiver mounted in the middle of the torso.
One accelerometer sensor is mounted behind the sender on theforearm. The accelerometer was sampled with 50 Hz.
Our test subject is a relative novice to Tai Chi havingaround 5 years background in other kinds of Kung Fu. As we
do not really want to access quality and are focusing on Tai
Chi beginner form movements, this presents no problem. We
recorded gestures from the beginner form of the Yang Tai Chi
style. The Yang style focuses on health benefits. The effectiveness has been proven in several medical studies (see [4]).
The gestures recorded are: opening form, parting wild
horse mane, single wip, grasping sparrow’s tail, waving arms
like clouds, high pat on horse back, brush knee, appear to
close crossed hands. Each gesture was recorded 10 times.

Results We present the data on a frame by frame (framesize
1 - one measurement) to the decision tree. It has been trainedwith 50 percent of the collected data, the rest has been used
for testing (test set 6500 Instances, overall recorded set:
13000 instances). The classification results for our sensor
tracking the forearm, a single Xsens placed on the wrist, a

Recognition Method For the analysis we calculate the features using a 1 sec. sliding window approach over the data.
The features calculated are median, variance, 75% percentile,
zero-crossing rate, number of peaks, mean peak hight, FFT
center of mass and the root mean square. From the magnetic
field sensor we use the ratios between the axis and the sum, as
7

The next steps in our research are to increase system range
through higher driving voltages and improve shielding of thecircuit against RF noise. We will also apply the system tofurther recognition tasks.

References
(a)

(b)

[1] Ryan Aylward and Joseph A. Paradiso. Sensemble: awireless, compact, multi-user sensor system for interactive dance. In NIME ’06: Proceedings of the 2006 conference on New interfaces for musical expression, pages
134–139, 2006.

(c)[2] Eric R. Bachmann, Robert B. McGhee, Xiaoping Yun,and Michael J. Zyda. Inertial and magnetic posture tracking for inserting humans into networked virtual environments. In VRST ’01: Proc. of the ACM symposium on
Virtual reality software and technology, pages 9–16, New
York, NY, USA, 2001. ACM Press.

(d)Figure 6. Four recorded Tai Chi movements: Parting Wild
Horse Mane (a), Single Wip (b), the Opening (c) and Moving
Hands Like Clouds (d)[3] M. Barry, A. Grunerbl, and P. Lukowicz. Wearable JointAngle Measurement with Modulated Magnetic Field
from L/C Oscilators. 4th International Workshop on
Wearable and Implantable Body Sensor Networks (Bsn
2007): March 26-28, Aachen,Germany, 2007.

described earlier. Concerning acceleration we use simply thethree axes. For the magnetic field we use all features except
the median and the zerocrossing rate. For the acceleration we
use all features except median peak height and fft center ofmass. This leaves us with 6 features per modality. On top of
the features calculated with sliding window, we train a KNN
classifier with k=3 using a 33% to 66% percentage split fortraining and test data. The classification results are smoothedusing a 10 sec. majority decision sliding window.
Results The results are summarized in the table below:
SensorRecognition rate
Gyro
64%
Acceleration86%
Acceleration and Gyro
90%
Magnetic
94%
Magnetic and Acceleration 100%
As expected from previous experiments, classification juston the accelerometer data brings 86 % correctly classified.
The magnetic field sensor is slightly better at 94 %. Combining the two modalities using the method described above,
we reach a classification rate of 100 %.

5[4] Wang C., Collet J., and Lau J. The effect of tai chi on
health outcomes in patients with chronic conditions: asystematic review. Arch Intern Med., 1:188–189, 2004.
[5] A. Hamaguchi, M. Kanbara, and N. Yokoya. User Localization Using Wearable Electromagnetic Tracker and Orientation Sensor. Wearable Computers, 2006 10th IEEE
International Symposium on, pages 55–58, 2006.
[6] E.A. Heinz, KS Kunze, M. Gruber, D. Bannach, and
P. Lukowicz. Using wearable sensors for real-time recognition tasks in games of martial arts–An initial experiment. Proc. IEEE CIG 2006, pages 98–102.
[7] C. Mattmann, O. Amft, H. Harms, G. Troster, and
F. Clemens. Recognizing Upper Body Postures using
Textile Strain Sensors. Wearable Computers, 2007 11th
IEEE International Symposium on, pages 1–8, 2007.
[8] Joseph A. Paradiso, Kai yuh Hsiao, and Ari Benbasat.
Tangible music interfaces using passive magnetic tags. In
NIME ’01: Proceedings of the 2001 conference on New
interfaces for musical expression, pages 1–4, Singapore,
2001. National University of Singapore.

Conclusion

The main conclusion of this work is that magnetic resonant coupling relative positioning is a useful activity recognition modality and can significantly improve recognition rates.
It can do so in an implementation that is well suited for thewearable environment and in fact not more complex then current inertial sensor based system.
While we obviously do not claim that our system will bebeneficial for all problem classes, we believe that there is abroad range of activity recognition task where relative position and orientation of body parts is an important information.

[9] G. Welch and E. Foxlin. Motion tracking: no silver bullet,
but a respectable arsenal. Computer Graphics and Applications, IEEE, 22(6):24–38, 2002.

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