Improving human movement sensing with micro models and domain knowledge

Human sensing is concerned with techniques for inferring information about humans from various sensing modalities. Examples of human sensing applications include human activity (or action) recognition, emotion recognition, tracking and localisation, identification, presence and motion detection, occupancy estimation, gesture recognition, and breath rate estimation. The first question addressed in this thesis is whether micro or macro models are a better design choice for human sensing systems. Micro models are models exclusively trained with data from a single entity, such as a Wi-Fi link, user, or other identifiable data-generating component. We consider micro and macro models in two human sensing applications, viz. Human Activity Recognition (HAR) from wearable inertial sensor data and device-free human presence detection from Wi-Fi signal data. The HAR literature is dominated by person-independent macro models. The few empirical studies that consider both micro and macro models evaluate them with either only one data-set or only one HAR algorithm, and report contradictory results. The device-free sensing literature is dominated by link-specific micro models, and the few papers that do use macro models do not evaluate their micro counterparts. Given the little and contradictory evidence, it remains an open question whether micro or macro models are a better design choice. We evaluate person-specific micro and person-independent macro models across seven HAR benchmark data-sets and four learning algorithms. We show that person-specific models (PSMs) significantly outperform the corresponding person-independent model (PIM) when evaluated with known users. To apply PSMs to data from new users, we propose ensembles of PSMs, which are improved by weighting their constituent PSMs according to their performance on other training users. We propose link-specific micro models to detect human presence from ambient Wi-Fi signal data. We select a link-specific model from the available training links, and show that this approach outperforms multi-link macro models. The second question addressed in this thesis is whether human sensing methods can be improved with domain knowledge. Specifically, we propose expert hierarchies (EHs) as an intuitive way to encode domain knowledge and simplify multi-class HAR, without negatively affecting predictive performance. The advantages of EHs are that they have lower time complexity than domain-agnostic methods and that their constituent classifiers are statistically independent. This property enables targeted tuning, and modular and iterative development of increasingly fine-grained HAR. Although this has inspired several uses of domain-specific hierarchical classification for HAR applications, these have been ad-hoc and without comparison to standard domain-agnostic methods. Therefore, it remains unclear whether they carry a penalty on predictive performance. We design five EHs and compare them to the best-known domain-agnostic methods. Our results show that EHs indeed can compete with more popular multi-class classification methods, both on the original multi-class problem and on the EHs' topmost levels

Mechanism, symmetry and topology of ordered phases in correlated systems = Mechanismus, Symmetrie und Topologie geordneter Phasen in korrelierten Systemen

In this thesis, interaction- and disorder-driven phase transitions are discussed with focus on the connection between the mechanism, the symmetry, and the topology of the resulting phase. Work is presented on the realization of a topological Mott insulator, selection rules for superconducting pairing, instabilities in oxide heterostructures, and the relation between the mechanism and the topology of superconductivity. Finally, the generalization to weakly disordered systems is analyzed

Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

Sensor and feature selection for an emergency first responders activity recognition system

Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms

A Fuzzy Classification Framework to Identify Equivalent Atoms in Complex Materials and Molecules

The nature of an atom in a bonded structure -- such as in molecules, in nanoparticles or solids, at surfaces or interfaces -- depends on its local atomic environment. In atomic-scale modeling and simulation, identifying groups of atoms with equivalent environments is a frequent task, to gain an understanding of the material function, to interpret experimental results or to simply restrict demanding first-principles calculations. While routine, this task can often be challenging for complex molecules or non-ideal materials with breaks of symmetries or long-range order. To automatize this task, we here present a general machine-learning framework to identify groups of (nearly) equivalent atoms. The initial classification rests on the representation of the local atomic environment through a high-dimensional smooth overlap of atomic positions (SOAP) vector. Recognizing that not least thermal vibrations may lead to deviations from ideal positions, we then achieve a fuzzy classification by mean-shift clustering within a low-dimensional embedded representation of the SOAP points as obtained through multidimensional scaling. The performance of this classification framework is demonstrated for simple aromatic molecules and crystalline Pd surface examples.Comment: Accepted manuscript in Journal of Chemical Physics. Repositories of the package (DECAF): DOI:10.17617/3.U7VKBM or https://gitlab.mpcdf.mpg.de/klai/deca

Using domain knowledge for interpretable and competitive multi-class human activity recognition

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems

Subject-dependent and -independent human activity recognition with person-specific and -independent models

The distinction between subject-dependent and subject-independent performance is ubiquitous in the Human Activity Recognition (HAR) literature. We test the hypotheses that HAR models achieve better subject-dependent performance than subject-independent performance, that a model trained with many users will achieve better subject-independent performance than one trained with a single user, and that one trained with a single user performs better for that user than one trained with this and other users by comparing four algorithms' subject-dependent and -independent performance across eight data sets using three different approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). Our analysis shows that PSMs outperform PIMs by 3.5% for known users, PIMs outperform PSMs by 13.9% and ensembles of PSMs by a not significant 2.1% for unknown users, and that the performance for known users is 20.5% to 48% better than for unknown users

A survey on the use of Artificial Intelligence for injury prediction in sports

Artificial Intelligence (AI) could play a significant role in injury prediction in sports due to its capabilities to detect and identify hidden patterns across multi-modal heterogeneous data sources. This paper aims at providing an up-to-date survey of the state-of-the-art in machine learning for injury predictions in sports. Finally, a number of considerations have been also drawn to discuss about the future research challenges required to be tackled to move this field forward

Monitoring emergency first responders' activities via gradient boosting and inertial sensor data

Emergency first response teams during operations expend much time to communicate their current location and status with their leader over noisy radio communication systems. We are developing a modular system to provide as much of that information as possible to team leaders. One component of the system is a human activity recognition (HAR) algorithm, which applies an ensemble of gradient boosted decision trees (GBT) to features extracted from inertial data captured by a wireless-enabled device, to infer what activity a first responder is engaged in. An easy-to-use smartphone application can be used to monitor up to four first responders' activities, visualise the current activity, and inspect the GBT output in more detail

Initial arch wires used in orthodontic treatment with fixed appliances

Background: Initial arch wires are the first arch wires to be inserted into the fixed appliance at the beginning of orthodontic treatment and are used mainly for the alignment of teeth by correcting crowding and rotations. With a number of different types of orthodontic arch wires available for initial tooth alignment, it is important to understand which wire is most efficient, as well as which wires cause least amount of root resorption and pain during the initial aligning stage of treatment. This is an update of the review entitledInitial arch wires for alignment of crooked teeth with fixed orthodontic braces, which was first published in 2010. Objectives: To assess the effects of initial arch wires for the alignment of teeth with fixed orthodontic braces, in terms of the rate of tooth alignment, amount of root resorption accompanying tooth movement, and intensity of pain experienced by patients during the initial alignment stage of treatment. Search methods: Cochrane Oral Health\u27s Information Specialist searched the following databases: Cochrane Oral Health\u27s Trials Register (to 5 October 2017), the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library, 2017, Issue 9), MEDLINE Ovid (1946 to 5 October 2017), and Embase Ovid (1980 to 5 October 2017. The US National Institutes of Health Trials Registry (ClinicalTrials.gov) and the World Health Organization International Clinical Trials Registry Platform were searched for ongoing trials. No restrictions were placed on the language or date of publication when searching the electronic databases. Selection criteria: We included randomised controlled trials (RCTs) of initial arch wires to align teeth with fixed orthodontic braces. We included only studies involving participants with upper or lower, or both, full arch fixed orthodontic appliances. Data collection and analysis: Two review authors were responsible for study selection, \u27Risk of bias\u27 assessment and data extraction. We resolved disagreements by discussion between the review authors. We contacted corresponding authors of included studies to obtain missing information. We assessed the quality of the evidence for each comparison and outcome as high, moderate, low or very low, according to GRADE criteria. Main results: For this update, we found three new RCTs (228 participants), bringing the total to 12 RCTs with 799 participants. We judged three studies to be at high risk of bias, and three to be at low risk of bias; six were unclear. None of the studies reported the adverse outcome of root resorption. The review assessed six comparisons. 1. Multistrand stainless steel versus superelastic nickel-titanium (NiTi) arch wires. There were five studies in this group and it was appropriate to undertake a meta-analysis of two of them. There is insufficient evidence from these studies to determine whether there is a difference in rate of alignment between multistrand stainless steel and superelastic NiTi arch wires (mean difference (MD) -7.5 mm per month, 95% confidence interval (CI) -26.27 to 11.27; 1 study, 48 participants; low-quality evidence). The findings for pain at day 1 as measured on a 100 mm visual analogue scale suggested that there was no meaningful difference between the interventions (MD -2.68 mm, 95% CI -6.75 to 1.38; 2 studies, 127 participants; moderate-quality evidence). 2. Multistrand stainless steel versus thermoelastic NiTi arch wires. There were two studies in this group, but it was not appropriate to undertake a meta-analysis of the data. There is insufficient evidence from the studies to determine whether there is a difference in rate of alignment between multistrand stainless steel and thermoelastic NiTi arch wires (low-quality evidence). Pain was not measured. 3. Conventional NiTi versus superelastic NiTi arch wires. There were three studies in this group, but it was not appropriate to undertake a meta-analysis of the data. There is insufficient evidence from these studies to determine whether there is any difference between conventional and superelastic NiTi arch wires with regard to either alignment or pain (low- to very low-quality evidence). 4. Conventional NiTi versus thermoelastic NiTi arch wires. There were two studies in this group, but it was not appropriate to undertake a meta-analysis of the data. There is insufficient evidence from these studies to determine whether there is a difference in alignment between conventional and thermoelastic NiTi arch wires (low-quality evidence). Pain was not measured. 5. Single-strand superelastic NiTi versus coaxial superelastic NiTi arch wires. There was only one study (24 participants) in this group. There is moderate-quality evidence that coaxial superelastic NiTi can produce greater tooth movement over 12 weeks (MD -6.76 mm, 95% CI -7.98 to -5.55). Pain was not measured. 6. Superelastic NiTi versus thermoelastic NiTi arch wires. There were three studies in this group, but it was not appropriate to undertake a meta-analysis of the data. There is insufficient evidence from these studies to determine whether there is a difference in alignment or pain between superelastic and thermoelastic NiTi arch wires (low-quality evidence). Authors\u27 conclusions: Moderate-quality evidence shows that arch wires of coaxial superelastic nickel-titanium (NiTi) can produce greater tooth movement over 12 weeks than arch wires made of single-strand superelastic NiTi. Moderate-quality evidence also suggests there may be no difference in pain at day 1 between multistrand stainless steel arch wires and superelastic NiTi arch wires. Other than these findings, there is insufficient evidence to determine whether any particular arch wire material is superior to any other in terms of alignment rate, time to alignment, pain and root resorption