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3D ROM Histograms

Wearable sensor technology, particularly inertial measurement sensors are used in various motion analysis including applications in sport,
medicine, and rehabilitation of patients after stroke. We are interested in deriving biomarkers to evaluate the patient’s recovery trend, motion performance and level of activity. See also our research projects about biosystems modelling for stroke rehabilitation here and here. In particular, the three-dimensional representation of the range of motion (ROM) provides
clinicians insights into patients motor function.  However, 3D representations of motion data derived during free-living activities or functional
therapies remain challenging and not intuitive.


In this research, we analyse the range of motion in patients after stroke. In particular, we develop an intuitive analysis tool based on orientation estimation using inertial sensor data and sensor fusion algorithms.

Functional ROM analysis approach using posture cubics. The functional ROM was defined by 27 cubics with a dimensionless edge length of 0.2. The ROM separates the space around the shoulder joint in three levels along the z-axis; the top level is the space above the shoulder joint, mid- and lower level represent space below the shoulder joint. Here, the posture cubics represent the ROM of the upper arm by quantifying the position of the elbow joint.


To gain insights in patients development, e.g. during activities of daily living (ADL), the 3D representation using cubics shows the motion distribution
of the upper arms of both body sides in 3D. We used color-code to illustrate the frequency of targeted cubics.

ROM represented in posture cubics of affected and non-affected body sides of patient ID6 during ADL. Colored posture cubics illustrate the time spent in an elbow orientation corresponding to the cubic volume during a recording day. The larger set of activated cubics at the affected side suggest here that the patient was exercising in the first seven rehab days (Days 1–7) more intensively compared to the last three recording days (Days 8–11). Also, the non-affected side showed a decline in activated posture cubics, which may indicate training fatigue or reduced motivation.

Work with us

For this research, we offer projects to investigate sensor-fusion algorithms, 3D-motion representation, and reconstruction from sparse sampled sensor-signals
and applications involving motion-animation, trajectory description, and trajectory evaluation.


Adrian Derungs, Corina Schuster-Amft, Oliver Amft, "A metric for upper extremity functional range of motion analysis in long-term stroke recovery using wearable motion sensors and posture cubics", Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), IEEE Xplore, March 2018.


Adrian Derungs