- Open Access
New generation of wearable goniometers for motion capture systems
© Tognetti et al.; licensee BioMed Central Ltd. 2014
Received: 8 March 2013
Accepted: 3 April 2014
Published: 11 April 2014
Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient’s home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented.
KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed.
The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations.
Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented.
Recently, a novel type of wearable sensor capable of detecting strain fields has been proposed [1–11]. Textile based deformation sensors can be produced by coating a thin layer of piezoresistive material on conventional fabrics [1, 4, 6] or by knitting conductive yarns with non-conductive yarns [2, 3, 7, 9]. In other works, conductive threads are stitched [10, 11] or attached [5, 8] to the top of the fabric. The main features of textile deformation sensors are flexibility and the preservation of the mechanical properties of the garments on which they are applied. Textile deformation sensors have several advantages compared to solid-state sensors: negligible weight, thickness and possibility of spreading a high number of measuring points over a flexible substrate. Sensing garments can be designed by applying sensor strips to specific locations on normal cloth. Changes in body shape and/or geometry due to human movements can in principle be estimated by reading variations on the measured strain.
However, it may be difficult to recover the relationship between fabric strains and biomechanics parameters, such as tri-dimensional geometry or angles. Several solutions have been proposed to address this issue. In  a multivariate interpolation on a grid of sensors was used. Laviola in  reviewed the algorithms for hand posture recognition; Gibbs and Asada  described a knee-sensing garment made with conductive fibers attached to flexible skin-tight fabrics. Mattmann et al.  combined a supervised learning algorithm with conductive thread sensors for the detection of torso movements. In  coated sensing fabrics are obtained by the integration of conductive elastomer (CE) materials on textile fabrics by an ad hoc screen printing procedure with variable topology. An application of CE sensors aimed at detecting the upper limb movement for neurological application is described in [16–18]. Generally, CE based sensing garments perform well for slow and wide movements, while the accuracy, transient time and hysteresis has limited their use in reconstructing fast and small movements, such as anatomical torsion. In addition, retrieving subject posture from textile strain measurements is highly affected by the relative position of sensors with respect to the joint being monitored. This issue has been addressed with the use of tight-fitting garments, which can reduce user acceptance especially in home rehabilitation contexts. Even in the case of adherent garments, it is not easy to obtain reproducible results due to the inevitable sliding/bending of the sensors on the textile and to the difficulty of wearing the garment in the same way after donning and doffing. These latter aspects have limited joint angle tracking due to the necessity of using a complex and long lasting calibration procedure  or have restricted sensing garment usage in gesture classification applications .
In , CE sensors were configured in a double layer structure capable of direct angular measurements for application in rehabilitation and biomechanics. Since these double layer angular sensors are less sensitive to precise positioning and to their intermediate bending profile, they have the potential to solve some of the issues described for textile based strain transducers.
In [7, 9, 20], Knitted Piezoresistive Fabric (KPF) sensors were demonstrated as a good tool for biomechanical and cardiopulmonary data acquisition and constitute an improvement on CEs. Compared to CE, KPF materials perform better in terms of response time, making them more suitable for wearable motion-capture applications. In fact, the transient time of CE sensors is very long and requires dedicated algorithms for predicting the final output after solicitation [6, 21]. In addition, producing CE sensors entails using trichloroethylene with consequent rigid constraints on manufacturing sensors. In , a data glove based on KPF used as strain sensors was developed and successfully tested in monitoring human hand gestures.
In this work we exploited KPF technology to create a new generation of wearable goniometers inspired by the methodology introduced in . The use of a new material led to a new theoretical approach compared to previous works aimed at improving the performance of CE sensors.
Single and double layer angular sensor working principle
This section describes the basic theoretical aspects of textile-based angular sensors. Both single and double layer configurations were analyzed according to the guidelines described in  for conductive elastomer sensors and differences between them are highlighted.
Textile-based sensors were produced using knitted piezoresistive fabrics (KPF) which contain 75% electro-conductive yarn (Belltron®;, produced by Kanebo Ltd) and 25% Lycra®;, manufactured as single jersey in a circular machine as described in previous works [7, 25]. Circular electronic seamless-wear knitting machines by Santoni T M  were used to produce piezoresistive fabrics due to their capability to handle yarns with high elastic recovery. Flat knitting machines are more sophisticate in term of stitch selections, but less efficient in the handling of elastic yarns and in the production time. Moreover a Santoni machinery requires only the use of 4/8 spools of yarns compared with warp knitting machines. A conductive bi-component fiber yarn based on polyamide loaded with carbon particles is used in combination with lycra to make this sensor. Piezoresistive fabric sensors change the electrical resistance according to the strain; the variation in electrical properties is due to the modification of the interconnections geometry inside the fabric structure. Usually this property can be observed in stretchable fabrics where the elongation of the fibers affects the flow of carrier inside the structure. When the conductivity of the yarn is due conductive particles as in bi-component fibers, the elongation of the yarn affects the charge transport mechanisms. The interconnections between the fibers and stitches are altered by the applied deformation. The elongation of the fabric modifies the distance between stitches as well as the arrangement of the fibers in the yarn leading to a different geometry of interconnections.
The custom-designed measurement setup is shown in Figure 6. A current generator supplies the two series of impedances with a constant current (I) and the acquisition system is a high input impedance stage constituted by two instrumentation amplifiers (A 1 and A 2). The outputs are amplified and the difference is measured in the following stage (A 3). Both the resistance difference and the single layer resistance values are then acquired by a National Instruments acquisition board. The goniometers (Figure 4) that we tested have an overall rest length of 100 m m, which corresponds to the distance between the current carrying pads. The rest distances between the voltage sensing pads B1B2 and B4B3 are 50 m m. The rest width and thickness are 10 m m and 0.5 m m, respectively.
Double layer device calibration
KPF samples were characterised in quasi-static conditions for elongation and flexion both for SL and DL configurations through dedicated bench testing setups. In addition, DL dynamic performance in terms of flexion were preliminary evaluated on a human subject in comparison with inertial measurement units (IMUs).
Quasi-static elongation test
Quasi-static flexion test
Quasi-static flexion characterization was carried out by relating RL 1, RL 2 and Δ R D L with the output of a commercial electrogoniometer. Electrogoniometers are commonly used as a gold standard for angle measuring in biomechanical applications . The KPF sensor was attached to a flexible substrate composed of woven fiberglass cloth and epoxy resin (i.e. standard printed circuit board material). Then, a two-axis electro-goniometer SG110 by Biometrics (±2°C accuracy) was attached to the opposite side of the flexible substrate. One extremity of this structure was clamped in a bench vice and the other remained free to pivot around, Figure 7(B). Starting from 0° the structure was bent to 90° through 13 steps. In each step, the sensor was held to rest for about 60 seconds and the average value of the recorded data was computed within the last 30 seconds. The test was performed both in flexion (from 0° to 90°) and extension (from 90° to 0°).
Stretching and bending data, acquired on SL and DL sensors using the setups described in the previous section, were analysed in order to assess the performance of:
SL in stretching (i.e. R S L variation with respect to the applied strain)
SL in bending and estimate the error by applying the relationships (1) truncate at the second order term in Δ α
DL in stretching and estimate the error by supposing that both layers have identical behaviour
DL in bending and estimate the error by applying the relationships (3) trunked at the third order term in Δ α
and in addition to:
Estimate the parameters q0, q1 and q2 introduced in (6) to calibrate the double layer device
Preliminarily assess dynamic performance of DL in bending
Hereinafter, to simplify the notation, R denotes the resistance of a single layer device or of one of the two layers (instead of R S L ), while Δ R indicates the resistance difference between the two layers (instead of Δ R D L )
Single layer resistance vs. stretching characteristics
In (9) and (10) p denotes the p t h trial executed for a certain deformation d i and P the total number of trials. To roughly estimate the SL electromechanical properties, vs. d i characteristic was approximated by a linear function and the deformation sensitivity (S d ) was computed as the slope of its linear approximation.
Using the single layer device as a goniometer
where k is k t h trial executed for a certain angle Δ α i and K the total number of trials.
trend was approximated by a linear regression in the least square sense and the linear regression slope, i.e. the angular sensitivity of the single layer sensor S Δ α S L , was computed.
Using the double layer device as a goniometer
The double layer device was tested in stretching to prove Δ R independence on elongation and in bending to verify the relationship between angles and resistance differences. Elongation/shortening and flexion/extension cycles were the same as described for the single layer sensor.
Parameters identification and calibration
The conditioning number gets worse, thus the second strategy, consisting in acting on the columns of A, is needed.
Numerical output (30) and (31) are characterized by the same values (they differ in non-significant digits) with very similar conditioning numbers.
A set of ten trials on different subjects was performed in three different conditions: slow-speed, medium-speed and high-speed flexions. For each trial, the statistics (33) and (34) were computed. A Student’s t-test was then performed to prove that the measurement samples obtained by the two different systems belonged to the same population (H0-hypothesis, i.e. X is a zero-mean random variable).
Single layer resistance vs. stretching characteristics
Using the single layer device as a goniometer
Using the double layer device as a goniometer
which corresponds to the estimated angular standard deviations of 2.4° and 1.48° respectively.
Parameter identification and calibration
Given this result, the parameters can be reduced further. The knowledge that a=a1=−a2 and q0=a3 guarantees the correct functionality of the device. The two values required can be computed by calibrating the device in two points. In this way a rapid calibration procedure can be executed directly on the body after the goniometer has been integrated into the garment. After subtracting the initial value q0 computed as the average value of the resistance difference for Δ α=0, it is sufficient to acquire the sensor output for a known angle (e.g. 90°) to compute a.
Both in the slow and fast knee flexion-extension, the double layer KPF goniometer performed well in angular measurements (maximum error of 5°) and managed to follow dynamic knee movements for compatible velocities with those of human movement.
Dynamic comparison between the KPF goniometer and IMUs in knee flexion-extension tasks
Double layer KPF goniometers performed better than single layer sensors in terms of quasi-static angle reconstruction (5.3° vs. 8.3°).
Commercial electrogoniometers, such as those used in our quasi-static comparison with a declared accuracy of ±2°, are widely used in ambulatory measurements of the joint range of motion (ROM) and movement frequency/velocity/acceleration for both clinical and occupational evaluations [29–31]. Studies on goniometer accuracy have shown errors of a few degrees, with great dependence on the sensor positioning and on the cross talk between joints . In  the evaluation of wrist ROM through electrogoniometers was performed with an error of between 2.2° and 6.2° over a flexion-extension range of 80°. Considering the widespread use of electrogoniometers, the reported errors are considered to be acceptable in clinical practice. The above reported KPF goniometer performances are slightly worse than those of commercial electrogoniometers, however they are still comparable with the ones accepted in goniometry applications.
KPF goniometers have also been compared with IMUs within dynamic trials. IMU-based joint kinematic estimations, widely described in , have a reconstruction accuracy that is lower than 3° for flexion-extension joint movements [35, 36], making the good agreement of our dynamic test very promising.
The results reported in Figure 15 confirmed that the performance in extension (1.48°) is more accurate than the flexion performance (2.4°). It should also be noted that the angular error of the double layer sensor is mainly due to a hysteretic phenomenon pointed out in Figure 15 (i.e. maximum distance between the increasing and decreasing curve holds 10.4 K Ω corresponding to 10.9°), since the standard deviations (38) and (39) are consistently smaller than those of the double layer sensor (σ M a x ).
The angular sensitivity was estimated in 960 Ω/° and is almost equal to that of a single goniometer. The maximum standard deviation is 3470 Ω which corresponds to an estimated angular standard deviation of 3.6° and represents a consistent improvement.
We have developed a novel type of wearable goniometer based on KPF technology. In this paper, the working principle and the theoretical approach of single and double layer configurations have been described. On the basis of this theory, sensors were designed and produced on a fabric substrate. A calibration procedure that takes into account the dissimilarity between the two layers was proposed. Both single layer and double layer goniometers were tested in quasi-static conditions and compared with standard instrumentation. Double layer KPF goniometers performed better in terms of angle reconstruction compared to single layer ones (5.3° vs. 8.3° maximum error). In addition, double layer sensors are not sensitive to elongation and thus they are more suitable for applications in wearable motion detection. A preliminary dynamical evaluation showed how the quasi-static results could be extended in dynamic conditions. In addition, we demonstrated the overall sensor performance could be further improved through the fusion of two KPF goniometers per joint. Future work will focus on extending dynamic modeling and testing.
This research was supported by the EU-ICT 7th framework project FP7-ICT-2011-7-287351 INTERACTION.
The Authors acknowledge Xavier Scarpelli for his support in graphical editing.
- De Rossi D, Carpi F, Lorussi F, Mazzoldi A, Scilingo E, Tognetti A: Electroactive fabrics for distributed, conformable and interactive systems. In Sensors, 2002. Proceedings of IEEE, Volume 2. IEEE; 2002:1608-1613.Google Scholar
- Bickerton M: Effects of fibre interactions on conductivity, within a knitted fabric stretch sensor. In Proceedings of IEEE Eurowearable Conference. IET; 2003.Google Scholar
- Wijesiriwardana R, Dias T, Mukhopadhyay S: Resistive fibre-meshed transducers. In Proceedings of the 7th IEEE International Symposium on Wearable Computers. IEEE Computer Society; 2003.Google Scholar
- Scilingo EP, Lorussi F, Mazzoldi A, De Rossi D: Strain-sensing fabrics for wearable kinaesthetic-like systems. IEEE Sensor J 2003,3(4):460-467. 10.1109/JSEN.2003.815771View ArticleGoogle Scholar
- Gibbs PT, Asada HH: Wearable conductive fiber sensors for multi-axis human joint angle measurements. J Neuroeng Rehabil 2005, 2: 7. 10.1186/1743-0003-2-7View ArticlePubMedPubMed CentralGoogle Scholar
- Tognetti A, Lorussi F, Bartalesi R, Quaglini S, Tesconi M, Zupone G, De Rossi D: Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation. J Neuroeng Rehabil 2005,. 2Google Scholar
- Pacelli M, Caldani L, Paradiso R: Textile piezoresistive sensors for biomechanical variables monitoring. In Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE. IEEE; 2006:5358-5361.View ArticleGoogle Scholar
- Mattmann C, Clemens F, Tröster G: Sensor for measuring strain in textile. Sensors 2008,8(6):3719-3732. 10.3390/s8063719View ArticlePubMed CentralGoogle Scholar
- Huang CT, Shen CL, Tang CF, Chang SH: A wearable yarn-based piezo-resistive sensor. Sensor Actuator Phys 2008,141(2):396-403. 10.1016/j.sna.2007.10.069View ArticleGoogle Scholar
- Gioberto G, Dunne L: Theory and characterization of a top-thread coverstitched stretch sensor. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on. IEEE; 2012:3275-3280.View ArticleGoogle Scholar
- Gioberto G, Dunne LE: Overlock-stitched stretch sensors: characterization and effect of fabric property. J Text Apparel, Technol Manage 2013.,8(3):Google Scholar
- Lorussi F, Rocchia W, Scilingo EP, Tognetti A, De Rossi D: Wearable, redundant fabric-based sensor arrays for reconstruction of body segment posture. IEEE Sensor J 2004,4(6):807-818. 10.1109/JSEN.2004.837498View ArticleGoogle Scholar
- LaViola J: A survey of hand posture and gesture recognition techniques and technology. Brown University, Providence, RI 1999.Google Scholar
- Mattmann C, Amft O, Harms H, Troster G, Clemens F: Recognizing upper body postures using textile strain sensors. In Wearable Computers, 2007 11th, IEEE International Symposium on. IEEE; 2007:29-36.Google Scholar
- Tognetti A, Bartalesi R, Lorussi F, De Rossi D: Body segment position reconstruction and posture classification by smart textiles. Trans Inst Meas Contr 2007,29(3–4):215-253.View ArticleGoogle Scholar
- Giorgino T, Tormene P, Lorussi F, De Rossi D, Quaglini S: Sensor evaluation for wearable strain gauges in neurological rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2009,17(4):409-415.View ArticlePubMedGoogle Scholar
- Cutolo F, Mancinelli C, Patel S, Carbonaro N, Schmid M, Tognetti A, De Rossi D, Bonato P: A sensorized glove for hand rehabilitation. In Bioengineering Conference, 2009 IEEE 35th Annual Northeast. IEEE; 2009:1-2.View ArticleGoogle Scholar
- Vanello N, Hartwig V, Tesconi M, Ricciardi E, Tognetti A, Zupone G, Gassert R, Chapuis D, Sgambelluri N, Scilingo EP, Giovannetti G, Positano V, Santarelli MF, Bicchi A, Pietrini P, De Rossi D, Landini D: Sensing glove for brain studies: design and assessment of its compatibility for fMRI with a robust test. IEEE ASME Trans Mechatron 2008,13(3):345-354.View ArticleGoogle Scholar
- Lorussi F, Galatolo S, De Rossi D: Textile-based electrogoniometers for wearable posture and gesture capture systems. IEEE Sensor J 2009,9(9):1014-1024.View ArticleGoogle Scholar
- Taccini N, Loriga G, Pacelli M, Paradiso R: Wearable monitoring system for chronic cardio-respiratory diseases. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. IEEE; 2008:3690-3693.View ArticleGoogle Scholar
- Tognetti A, Lorussi F, Tesconi M, De Rossi D: Strain sensing fabric characterization. In Sensors, 2004. Proceedings of IEEE. IEEE; 2004:527-530.View ArticleGoogle Scholar
- Carbonaro N, Greco A, Anania G, Dalle Mura G, Tognetti A, Scilingo EP, De Rossi D, Lanatà A: Unobtrusive physiological and gesture wearable acquisition system: a preliminary study on behavioral and emotional correlations. GLOBAL HEALTH 2012, The First International Conference on Global Health Challenges 2012, 88-92.Google Scholar
- Lipschiutz MM: Differential Geometry. McGraw-Hill; 1984.Google Scholar
- Lorussi F, Galatolo S, De Rossi D, Bartalesi R: Modeling and characterization of extensible wearable textile-based electrogoniometers. IEEE Sensor J 2013.Google Scholar
- Pacelli M, Caldani L, Paradiso R: Performances evaluation of piezoresistive fabric sensors as function of yarn structure. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE; 2013:6502-6505.View ArticleGoogle Scholar
- Circular knitting machines [http://www.santoni.com]
- Electrogoniometers [http://www.biometricsltd.com/gonio.htm]
- XSENS MTw [http://www.xsens.com/en/general/mtw]
- Hansson G, Mikkelsen S: Kinematic evaluation of occupational work. Adv Occup Med Rehabil 1997, 3: 57-69.Google Scholar
- Yen TY, Radwin RG: Comparison between using spectral analysis of electrogoniometer data and observational analysis to quantify repetitive motion and ergonomic changes in cyclical industrial work. Ergonomics 2000, 43: 106-132. 10.1080/001401300184684View ArticlePubMedGoogle Scholar
- Marras WS, Schoenmarxlin RW: Wrist motions in industry. Ergonomics 1993,36(4):341-351. 10.1080/00140139308967891View ArticlePubMedGoogle Scholar
- Buchholz B, Wellman H: Practical operation of a biaxial goniometer at the wrist joint. Hum Factors: J Hum Factors Ergon Soc 1997, 39: 119-129. 10.1518/001872097778940696View ArticleGoogle Scholar
- Johnson PW, Jonsson P, Hagberg M: Comparison of measurement accuracy between two wrist goniometer systems during pronation and supination. J Electromyogr Kinesiol 2002,12(5):413-420. 10.1016/S1050-6411(02)00031-7View ArticlePubMedGoogle Scholar
- Luinge H, Veltink P, Baten C: Ambulatory measurement of arm orientation. J Biomech 2007, 40: 78-85. 10.1016/j.jbiomech.2005.11.011View ArticlePubMedGoogle Scholar
- O’Donovan KJ, Kamnik R, O’Keeffe DT, Lyons GM: An inertial and magnetic sensor based technique for joint angle measurement. J Biomech 2007,40(12):2604-2611. 10.1016/j.jbiomech.2006.12.010View ArticlePubMedGoogle Scholar
- Picerno P, Cereatti A, Cappozzo A: Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Posture 2008,28(4):588-595. 10.1016/j.gaitpost.2008.04.003View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.