## 6 Conclusions  
In this paper, an upper limb kinematic model that resembles a robotic arm with rigid links is proposed. It is formed as a 7 DoF kinematic chain, while the Denavit-Hartenberg configuration is adopted to describe this model. Furthermore, the Forward Kinematics matrices that can give the position and orientation of each upper limb segment are derived.  
For this model, the shoulder, the elbow and the wrist joint angles are estimated by exploiting the upper arm, forearm and hand segments' orientation vectors, respectively. For this reason, inertial sensors are attached to the upper limb segments, in order to capture theirs motion. This is finally accomplished by a two steps procedure. At first, a sensor fusion algorithm is applied to the inertial sensors' data along with a complementary filter, in order to extract each individual upper limb's segment orientation. Then, these orientations contribute to the estimation of the rotational DoFs that are defined in the aforementioned upper limb model.  
This concludes to the 3D reconstruction of the upper limb model's motion by applying the Forward Kinematics of the DH configuration. The 3D reconstruction results, the trajectory tracking and the visualization of a subject's upper limb for both the prototype motion capture sensor node and the off-the-shelf Shimmer3 sensor unit, are performed, for comparison reasons. The accuracy of the results derived by the custom-made sensor node is also validated with an RMSE of 0.02 m and a maximum error of 0.07 m, when these are compared to the results of the similar commercial wireless motion capturing device.  
These validation results demonstrate the accuracy of the inertial technology based motion analysis, which is presented in this work, and can be a guide for further improvement. The same methodology could also be extended to estimate the lower limb, or the whole body movements in the 3D space, using additional sensors and similar robotic modeling of the human body segments.  
Conceptualization, O.T. and E.D.; methodology, O.T. and K.G.; software, O.T., K.G. and N.E.; investigation, O.T., K.G., N.E. and E.D.; data curation, O.T., K.G. and N.E.; writing-original draft preparation, O.T., K.G. and E.D.; writing-review and editing, O.T., K.G. and E.D.; visualization, O.T. and K.G.; supervision, E.D.; project administration, E.D.; funding acquisition, O.T., K.G., N.E. and E.D. All authors have read and agreed to the published version of the manuscript.  
This work was performed under the framework of the \"EDBM34 Call - Support research focusing on young researchers\" project entitled \"Design and implementation of a complementary sensing system for physiotherapy procedure monitoring through smart portable devices\" (MIS 5005357), co-funded by the European Social Fund (ESF) and National Resources.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3D & 3-Dimensional \\ CSMA/CA & Carrier Sense Multiple Access/Collision Detection \\ DH & Denavit-Hartenberg \\ DoF & Degrees of Freedom \\ I\({}^{2}\)C & Inter Integrated Circuit \\ IMU & Inertial Measurement Unit \\ LoS & Line of Sight \\ MEMS & Micro-Electro-Mechanical System \\ OLE & Optical Linear Encoders \\ PCA & Principal Component Analysis \\ TDMA & Time Division Multiple Access \\ WSN & Wireless Sensor Network \\ \end{tabular}