INERTIAL SENSOR BASED IDENTIFICATION OF

HUMAN MOVEMENTS

Ivo Stancic

, Josip Music

, Ana Kuzmanic Skelin

, Tea Marasovic

, Norberto Salgado

Tamara Supuk

and Vlasta Zanchi

Faculty of Electrical engineering, Mechanical Engineering and Naval Architecture – FESB

University of Split, Rudjera Boskovica bb, Split, Croatia

Department of Electronic, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal

Keywords: Inertial sensors, Head movement, Standing up movement, Kalman filtering, Spine load during sitting and

standing.

Abstract: The scope of this paper is the presentation of experiments which involve measurements and identification of

human movements by using the inertial sensors. We describe the purpose, design and obtained results of

two experiments, as well as our future plans which include the exploration of the forces acting at spine

segments by measurements with inertial sensors. The first experiment implemented the method for

measuring the range of motion (RoM) of head in transverse plane (Kuzmanic, 2007). It was done in the

Laboratory of Biomechanics and Automatic Control – LaBACS, University of Split. In the second

experiment we analyzed the standing – up movement and we used the robot assistive device for the support

of human while performing the standing – up task. Measurements for purposes of this experiment were done

in the Laboratory of Biomedical Engineering and Robotics, University of Ljubljana. We have proposed the

new method which uses the Extended Kalman filtering for combining the data acquired from inertial sensor

measurements of standing – up movement with data from the dynamic human body model (Music, 2008).

Our plans regarding the next experiment are focused on the identification of the spinal load during sitting

and standing, by using the inertial sensors measurement system.

1 MEASUREMENT OF THE

HEAD’S RANGE OF MOTION

The measurement of range of motion (RoM) and

static posture of the head gives important physical

parameters for clinical assessment and diagnosis

related to cervical spine functions. In literature, this

movement is referred as a cervical range of motion.

The detection of an abnormal RoM or asymmetrical

patterns is an essential for preventing cervical

dysfunction (McAviney, 2005; Wu, 2007).

One of our aims was to investigate the feasibility of

the use of inertial sensors in routine clinical

assessments. Therefore, our goal was to design the

system based on inertial sensors and to propose the

method for measuring the range of motion (RoM) of

head in transverse plane. The measurement was

performed using single inertial measurement unit

MTx XSens sensor (XSens Motion Technologies,

Netherlands), Fig. 1. Specialized software for sensor

data acquisition, with high visualization abilities has

been developed in LaBACAS. MTx XSens sensor

can provide useful, noninvasive measurement of

head motion in three cardinal planes for the fast

evaluation of disturbances related to head/neck

problems and cervical dysfunctions. The advantage

of the proposed method over standard methods is the

ability to measure unilateral RoM of the head. This

technique overcomes the limits of ‘gold standard’

measurement devices by estimating the neutral

position, which is assumed to be a nontrivial problem

in standard RoM measurement. In addition, a

proposal for use of sensor for visual feedback RoM

assessment is presented. LaBACS MTx Software

was developed by in-house, to control the operation

of the inertial measurement unit (IMU), acquire the

data and display them in the real time. Program was

developed under Microsoft Visual Studio 2005, using

MFC (Microsoft Foundation Class). Fig. 2. shows a

frame of running software. Measurement was done

on 6 subjects without any known symptoms of

300

Stancic I., Music J., Kuzmanic Skelin A., Marasovic T., Salgado N., Supuk T. and Zanchi V. (2009).

INERTIAL SENSOR BASED IDENTIFICATION OF HUMAN MOVEMENTS.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 300-303

DOI: 10.5220/0001777403000303

 SciTePress

cervical dysfunction. Five repetitions of movements

were analyzed and averaged for each subject in order

to eliminate the variability during movement

recording. In accordance with standard, total RoM is

calculated by subtraction of maximal and minimal

angle, or by summation of left and right RoM,

assuming that the neutral position angle is known.

During the measurement neutral position is identified

statistically, over time interval of five repetitions of

cyclic RoM movement, Fig. 3 b).

Figure 1: Measurement setup: Subject with sensor

mounted on a cap.

Figure 2: User interface of LaBACS MTx Software.

1.1 Results of the First Experiment

The results of the measurement on 6 asymptomatic

subjects are given in Table 1. Resulting angles of

each group are described in terms of mean RoM

± standard deviation [

], for the movement on the

left (LRoM) and right side (RRoM). The results of

the present study demonstrate similar ranges of

motion as found in literature (Dvir, 2000), although

the existing results are obtained with different

instrumentation. Measurement of individual neutral

position has a standard deviation ranging from

minimally ±1.12

to maximally ±3.36

. These results

imply that the subjects are able to return the head to a

self-defined neutral position. Therefore, the

measurement method of head motion based on

inertial sensors is valid for current application of

RoM in transverse plane and is suitable for

measurement of head neutral position, as well.

Table 1: RoM results for 6 asymptomatic subjects.

left side:

LRoM

right side:

RRoM

RoM (total) LRoM/

RRoM

1) 2)

73.02

± 7.61

74.34

± 9.44

147.28

± 15.51

147.36

± 15.54

1.022

± 0.096

1) α

– α

where α

is maximal and α

is minimal head

angle; 2) LRoM + RRoM

Figure 3: a) Recorded angles of cyclic movement; b)

Histogram computation of head neutral position and

endpoint angles.

2 KINEMATIC MEASUREMENTS

OF STANDING – UP MOTION

Number of aiding systems has been developed for

the purpose of standing–up support. Recently, robot

assistive devices have been introduced and their

benefits demonstrated. In acquisition systems, the

kinetic and kinematic parameters of the subject are

required for operation of the robot control algorithm.

Kinematic measurements are usually performed with

INERTIAL SENSOR BASED IDENTIFICATION OF HUMAN MOVEMENTS

301

optical motion analysis systems that are unsuitable

for clinical applications. Therefore, introduction of

miniature, low cost inertial sensors (accelerometers

and gyroscopes) as a body mounted sensors, has

shown to be promising.

We propose a new approach in which the

Extended Kalman filtering (EKF) technique is used

to fuse data acquired from inertial sensor

measurements with data from the dynamic human

body model (Music, 2008). In this way we believe

that better kinematic measurements in ambulatory

settings are possible. We named the approach Model

Based Inertial Sensing - MoBIS.

The proposed human body model consists of

shank, thigh and HAT (Head-Arms-Trunk)

segments, Fig. 4.

Figure 4: Measurement setup: (1) linear infrared cameras,

(2) HAT inertial sensing unit, (3) thigh inertial sensing

unit, (4) shank inertial sensing unit, (5) AMTI OR6-6-1

force plate, (6) seat, (7) JR3 40E15 force sensor, (8)

standing-up robot assistive device.

The segments are assumed to be rigid bodies

with their masses contained at center of mass

(CoM). Segment masses, lengths, moments of inertia

and CoM positions are defined using anthropometric

data. Three joints (ankle, knee and hip) are assumed

to be ideal pin joints with no added friction during

rotation. The model is in contact with its

environment only by the distal end of the shank

segment i.e. by subject’s feet. The assumption of

symmetry of sit-to-stand motion in respect to sagittal

plane was adopted in modeling phase. This

assumption enables the measurements to be carried

out only on one side of the body and results

projected on the other side. The symmetry

assumption does introduce certain error.

2.1 Results of the Second Experiment

The method is validated on both simulated and

measured data. The presented results (Figures 5, 6

and Table 2) show that Model Based Inertial

Sensing (MoBIS) in robot assisted standing-up is a

reliable alternative to optical measurements systems

for motion kinematics assessment. To improve

method performance in terms of accuracy and

reliability, further development (e.g. extensive

testing on a group of healthy and impaired subjects,

introduction of adaptive EKF) is suggested (Music,

2008).

Figure 5: Comparison of actual and measured angles.

Figure 6: Comparison of Optotrak and EKF data.

Table 2: Measurement error.

Shank

RMSE

[deg]

Thigh

RMSE

[deg]

HAT

RMSE

[deg]

Normal/self-selected standing up speed

Meas. 01 6.1 4.1 3.8

Meas. 02 2.1 7.8 6.8

Meas. 03 2.4 5.2 6.8

Meas. 04 3.7 3.5 5.9

Average 1 3.6 5.2 5.8

Fast standing up

Meas. 05 1.4 2.5 3.2

Meas. 06 4.8 2.6 4.7

Meas. 07 4.9 2.4 5

Meas. 08 3.9 2.1 5

Average 2 3.8 2.4 4.5

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3 DISCUSSION

AND CONCLUSIONS

Results obtained in described experiments show that

inertial sensors can be implemented into different

measurement systems and bio – devices as reliable

and yet inexpensive tool for identification of

versatile human movements. Our future work

regarding the implementation of inertial sensors

includes the identification of the spinal load during

sitting and standing.

3.1 Segmental Spine Load: Model

and Force Analysis

The main idea is to explore forces acting at single

spine segments. All the measurement procedures,

used in the research, will be noninvasive. The

identification of the single spine segment

coordinates will be done using the inertial sensors.

The later stages will also include the analysis and

calculations of corresponding forces, and therefore,

to that end, ground reaction forces will be measured

(Supuk, 2002). Research will be performed in static

and dynamic conditions, on sitting and standing

subject, Figures 7 and 8. Partial differential

equations will provide mathematical support during

the modeling process, keeping in mind that we are

dealing with compartmental system. Configuration

of the spine, that will be identified based on the

results of measurements obtained by five different

sensor outputs, along with the seat reaction forces

will serve as input parameters for the calculations of

forces acting at the diverse points of the spine (33

vertebrae including the five that are fused to form

the sacrum (the others being separated by

intervertebral discs) and the four bones which form

the tailbone.).

Figure 7: Standing subject spine model.

Figure 8: Musculoskeletal model used to identify spine

configuration of the subject in the seated position.

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