Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation

Over the last years, inertial sensing has proven to be a suitable ambulatory alternative to traditional human motion tracking based on optical position measurement systems, which are generally restricted to a laboratory environment. Besides many advantages, a major drawback is the inherent drift caused by integration of acceleration and angular velocity to obtain position and orientation. In addition, inertial sensing cannot be used to estimate relative positions and orientations of sensors with respect to each other. In order to overcome these drawbacks, this study presents an Extended Kalman Filter for fusion of inertial and magnetic sensing that is used to estimate relative positions and orientations. In between magnetic updates, change of position and orientation are estimated using inertial sensors. The system decides to perform a magnetic update only if the estimated uncertainty associated with the relative position and orientation exceeds a predefined threshold. The filter is able to provide a stable and accurate estimation of relative position and orientation for several types of movements, as indicated by the average rms error being 0.033 m for the position and 3.6 degrees for the orientation

A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors

Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review.

Background: Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties. Methods: A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle. Results: Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy. Conclusion: Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error < 5°). Additional research and standardisation is required to guide clinical application

[Herkenning van visuele karakteristieken van infrarood spectra door kunstmatige neurale netwerken en partiele kleinste kwadraten regressie.]

Abstract niet beschikbaarThe usefulness of artificial neural networks (ANN) and partial least squares regression (PLS) for computerized interpretation of infrared (IR) spectra has been studied. Experiments have been carried out to establish the capabilities of these methods to recognize characteristic band shapes and patterns as used for the interpretation by experts. Spectra have been classified by (i) the complete spectral profile (ii) the band pattern in a limited preselected region and (iii) individual band shapes. The results are compared with classifications using computer generated frequency/intensity-structure correlations and as performed by experienced spectroscopists. Classification by skilled interpretators is found to be superior in all cases but a significant improvement of the results from ANN and PLS is established compared with predictions obtained from frequency/intensity-structure correlations. Differences in scores between ANN and PLS were small when full spectra or limited spectral regions are considered. Networks scored better in recognizing individual bands. Both the absorption frequency and the band width play an important role in the recognition process.RIV

Bruikbaarheid van kunstmatige neurale netwerken voor de identititeitsbevestiging van infraroodspectra

Onderzoek is uitgevoerd naar de bruikbaarheid van kunstmatige neurale netwerken als criterium voor de identiteitsbevestiging van infrarood-(IR) spectra. Doel van het onderzoek is de identificatie van spectra met hoge ruisniveaus, verkregen met behulp van gecombineeerde gaschromatografie (GC)IR spectrometrie. Neurale netwerken zijn getraind op GC/IR spectra van Clenbuterol, Fluoranthene en Perylene. De resultaten zijn vergeleken met classificatie door middel van "peak matching-" en bibliotheekzoekprocedures. Peak matching bleek de meest betrouwbare methode voor de identificatie van sterk gelijkende spectra. Voorwaarde is echter een laag ruisniveau en een hoge spectrale resolutie. Dit geldt eveneens voor bibliotheekzoekmethoden. Neurale netwerken bleken minder gevoelig voor ruis en daarom meer geschikt voor bevestiging van de identiteit van IR-spectra van sporenhoeveelheden.The utility of artificial neural networks (ANN) as a tool for confirmation of the identity of infrared (IR) spectra has been investigated. The main goal of the study is the identification of spectra with relatively high noise levels, obtained from gas chromatography combined with IR spectrometric detection. Networks were trained for GC/IR spectra of Clenbuterol, Fluoranthene and Perylene as representatives of compounds for which identification in real world samples is demanded occasionally. Results have been compared with classification by peak matching and library search methods. Peak matching appears to be the most discriminative method to distinguish between closely resembling spectra, but only in case of high signal-to-noise ratio and resolution. Similar conclusions are drawn for library search identification. ANN-models are less sensitive to spectral noise and hence most suited to be used for confirmation and identification of spectra obtained intrace analysis.RIV

Recognizing visual aspects of infrared spectra with neural networks

Abstract niet beschikbaarThis document describes how neural networks can be trained to classify and recognize infrared spectra. Backpropagation was used as the neural network type. The effect of noise on the recognition capabilities of a network has been investigated by generating 150 spectra with various noise levels out of 3 standard spectra. The trained network appeared to be capable of recognizing spectra correctly up to a noise level of 70%. Recognition appears to be correct up to a noise lebel of 70%. The classifying capabilities of backpropagation of spectra have been studied by training a network with 30 spectra, equally divided over three classes. Fourteen other spectra were used as a control set. Only one spectrum was found to be incorrectly classified. The preliminary conclusion is that neural networks are a useful addition to standard pattern matching techniques, especially for recognizing visual aspects.RIV

Susceptibility to chronic mucus hypersecretion, a genome wide association study

Background: Chronic mucus hypersecretion (CMH) is associated with an increased frequency of respiratory infections, excess lung function decline, and increased hospitalisation and mortality rates in the general population. It is associated with smoking, but it is unknown why only a minority of smokers develops CMH. A plausible explanation for this phenomenon is a predisposing genetic constitution. Therefore, we performed a genome wide association (GWA) study of CMH in Caucasian populations. Methods: GWA analysis was performed in the NELSON-study using the Illumina 610 array, followed by replication and metaanalysis in 11 additional cohorts. In total 2,704 subjects with, and 7,624 subjects without CMH were included, all current or former heavy smokers ($20 pack-years). Additional studies were performed to test the functional relevance of the most significant single nucleotide polymorphism (SNP). Results: A strong association with CMH, consistent across all cohorts, was observed with rs6577641 (p = 4.25x10⎺⁶, OR = 1.17), located in intron 9 of the special AT-rich sequence-binding protein 1 locus (SATB1) on chromosome 3. The risk allele (G) was associated with higher mRNA expression of SATB1 (4.3x10⎺⁹) in lung tissue. Presence of CMH was associated with increased SATB1 mRNA expression in bronchial biopsies from COPD patients. SATB1 expression was induced during differentiation of primary human bronchial epithelial cells in culture. Conclusions: Our findings, that SNP rs6577641 is associated with CMH in multiple cohorts and is a cis-eQTL for SATB1, together with our additional observation that SATB1 expression increases during epithelial differentiation provide suggestive evidence that SATB1 is a gene that affects CMH