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SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the ‘SPHERE House’ in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).

IntroductionLimitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the ‘TV task’, designed to track changes in ecologically valid conversations with disease progression.Methods and analysisCUBOId is a longitudinal observational study. Participants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over timespans of 8–25 months. At two time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone.Ethics and disseminationCUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is supported by the National Institute for Health Research Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.

IntroductionLimitations in effective dementia therapies mean that early diagnosis and monitoring are critical for disease management, but current clinical tools are impractical and/or unreliable, and disregard short-term symptom variability. Behavioural biomarkers of cognitive decline, such as speech, sleep and activity patterns, can manifest prodromal pathological changes. They can be continuously measured at home with smart sensing technologies, and permit leveraging of interpersonal interactions for optimising diagnostic and prognostic performance. Here we describe the ContinUous behavioural Biomarkers Of cognitive Impairment (CUBOId) study, which explores the feasibility of multimodal data fusion for in-home monitoring of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). The report focuses on a subset of CUBOId participants who perform a novel speech task, the “TV task”, designed to track changes in ecologically valid conversations with disease progression.Methods and AnalysisParticipants have diagnoses of MCI or AD, and controls are their live-in partners with no such diagnosis. Multimodal activity data were passively acquired from wearables and in-home fixed sensors over periods ranging from 8 to 25 months. At 2 time points participants completed the TV task over 5 days by recording audio of their conversations as they watched a favourite TV programme, with further testing to be completed after removal of the sensor installations. Behavioural testing is supported by neuropsychological assessment for deriving behavioural ground truths on cognitive status. Deep learning will be used to generate fused multimodal activity-speech embeddings for optimisation of diagnostic and predictive performance from speech alone.Ethics and disseminationCUBOId was approved by an NHS Research Ethics Committee (Wales REC; ref: 18/WA/0158) and is sponsored by University of Bristol. It is also supported by the National Institute for Health Research (NIHR) Clinical Research Network West of England. Results will be reported at conferences and in peer-reviewed scientific journals.ARTICLE SUMMARYStrengths and limitationsTo our knowledge, this is the first study to simultaneously characterise longitudinal, ecologically valid diagnostic trajectories across a broad range of behavioural domains relevant to cognitive decline “in the wild”, allowing construction of shared multimodal embeddings for dementia diagnosis from speech aloneParticipants’ live-in partners are contextually matched controls, accounting for differences across participants’ home environments and lifestylesMeasurements of cognitive status from neuropsychological testing form ground truths for behavioural biomarkers of cognitive declineLimited sample size and restricted demographics may confer limited generalisability of findings to other populationsBehavioural and neuropsychological testing timelines disrupted and desynchronised due to COVID-19

Cross-lingual text representations have gained popularity lately and act as the backbone of many tasks such as unsupervised machine translation and cross-lingual information retrieval, to name a few. However, evaluation of such representations is difficult in the domains beyond standard benchmarks due to the necessity of obtaining domain-specific parallel language data across different pairs of languages. In this paper, we propose an automatic metric for evaluating the quality of cross-lingual textual representations using images as a proxy in a paired image-text evaluation dataset. Experimentally, Backretrieval is shown to highly correlate with ground truth metrics on annotated datasets, and our analysis shows statistically significant improvements over baselines. Our experiments conclude with a case study on a recipe dataset without parallel cross-lingual data. We illustrate how to judge cross-lingual embedding quality with Backretrieval, and validate the outcome with a small human study., Comment: SIGIR 2021

Acceleration data for activity recognition typically are collected on battery-powered devices, leading to a trade-off between high-accuracy recognition and energy-efficient operation. We investigate this trade-off from a feature selection perspective, and propose an energy-efficient activity recognition framework with two key components: a detailed energy consumption model and a number of feature selection algorithms. We evaluate the model and the algorithms using Random Forest classifiers to quantify the recognition accuracy, and find that the multi-objective Particle Swarm Optimization algorithm achieves the best results for the task. The results show that by selecting appropriate groups of features, energy consumption for computation and data transmission is reduced by an order of magnitude compared with the raw-data approach, and that the framework presents a flexible selection of feature groups that allow the designer to choose an appropriate accuracy-energy trade-off for a specific target application.

Multi-language recipe personalisation and recommendation is an under-explored field of information retrieval in academic and production systems. The existing gaps in our current understanding are numerous, even on fundamental questions such as whether consistent and high-quality recipe recommendation can be delivered across languages. In this paper, we introduce the multi-language recipe recommendation setting and present grounding results that will help to establish the potential and absolute value of future work in this area. Our work draws on several billion events from millions of recipes and users from Arabic, English, Indonesian, Russian, and Spanish. We represent recipes using a combination of normalised ingredients, standardised skills and image embeddings obtained without human intervention. In modelling, we take a classical approach based on optimising an embedded bi-linear user-item metric space towards the interactions that most strongly elicit cooking intent. For users without interaction histories, a bespoke content-based cold-start model that predicts context and recipe affinity is introduced. We show that our approach to personalisation is stable and easily scales to new languages. A robust cross-validation campaign is employed and consistently rejects baseline models and representations, strongly favouring those we propose. Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work. We believe that this is the first large-scale work that comprehensively considers the value and potential of multi-language recipe recommendation and personalisation as well as delivering scalable and reliable models., Comment: 5 tables

One of the main shortcomings of received signal strength-based indoor localisation techniques is the labour and time cost involved in acquiring labelled `ground-truth' training data. This training data is often obtained through fingerprinting, which involves visiting all prescribed locations to capture sensor observations throughout the environment. In this work, the authors present a helmet for localisation optimisation (H4LO): a low-cost robotic system designed to cut down on said labour by utilising an off-the-shelf light detection and ranging device. This system allows for simultaneous localisation and mapping, providing the human user with accurate pose estimation and a corresponding map of the environment. The high-resolution location estimation can then be used to train a positioning model, where received signal strength data is acquired from a human-worn wearable device. The method is evaluated using live measurements, recorded within a residential property. They compare the groundtruth location labels generated automatically by the H4LO system with a camera-based fingerprinting technique from previous work. They find that the system remains comparable in performance to the less efficient camera-based method, whilst removing the need for time-consuming labour associated with registering the user's location.

This study is concerned with astronomical time-series called light-curves that represent the brightness of celestial objects over a period of time. We consider the task of finding anomalous light-curves of periodic variable stars. We employ a Hierarchical Gaussian Process to create a general and stable model of time-series for anomaly detection, and apply this approach to the light-curve problem. Hierarchical Gaussian Processes require only a few additional parameters compared to conventional Gaussian Processes and incur negligible additional computational complexity. Moreover, since the additional parameters are objectively optimised in a principled probabilistic framework one does not need to resort to grid searches for parameter selection. Experimentally, we demonstrate that our approach outperforms several baselines on both synthetic and light-curve data. Of particular interest is that the proposed method generalises very well from small subsets of the data, achieving near perfect precision of outlier detection even with as few as seven instances.

Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag., Comment: Accepted to MLSP 2018

Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people&rsquo, s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world.

The SPHERE project is devoted to advancing eHealth in a smart-home context, and supports full-scale sensing and data analysis to enable a generic healthcare service. We describe, from a data-science perspective, our experience of taking the system out of the laboratory into more than thirty homes in Bristol, UK. We describe the infrastructure and processes that had to be developed along the way, describe how we train and deploy Machine Learning systems in this context, and give a realistic appraisal of the state of the deployed systems.

Here we examine astronomical time-series called light-curve data, which represent the brightness of celestial objects over a period of time. We focus specifically on the task of finding anomalies in three sets of light-curves of periodic variable stars. We employ a hierarchical Gaussian process to create a general and stable model of time series for anomaly detection, and apply this approach to the light curve problem. Hierarchical Gaussian processes require only a few additional parameters than Gaussian processes and incur negligible additional computational complexity. Additionally, the additional parameters are objectively optimised in a principled probabilistic framework. Experimentally, our approach outperforms several baselines and highlights several anomalous light curves in the datasets investigated.

There's a widely known need to revise current forms of healthcare provision. Of particular interest are sensing systems in the home, which have been central to several studies. This article presents an overview of this rapidly growing body of work, as well as the implications for machine learning, with an aim of uncovering the gap between the state of the art and the broad needs of healthcare services in ambient assisted living. Most approaches address specific healthcare concerns, which typically result in solutions that aren't able to support full-scale sensing and data analysis for a more generic healthcare service, but the approach in this article differs from seamlessly linking multimodel data-collecting infrastructure and data analytics together in an AAL platform. This article also outlines a multimodality sensor platform with heterogeneous network connectivity, which is under development in the sensor platform for healthcare in a residential environment (SPHERE) Interdisciplinary Research Collaboration (IRC).

There has been significant recent interest in sensing systems and ‘smart environments’, with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or activities that the resident(s) of the home are engaged in, which may in turn be used for determining normal or abnormal patterns of behaviour (e.g. in a health-care setting). Classification algorithms, such as Conditional Random Field (CRFs), typically consider sensor activations as features but these are often treated as if they were independent, which in general they are not. Our hypothesis is that learning patterns based on combinations of sensors will be more powerful than single sensors alone. The exhaustive approach – to take all possible combinations of sensors and learn classifier weights for each combination – is clearly computationally prohibitive. We show that through the application of signal processing and information-theoretic techniques we can learn about the sensor topology in the home (i.e. learn an adjacency matrix) which enables us to determine the combinations of sensors that will be useful for classification ahead of time. As a result we can achieve classification performance better than that of the exhaustive approach, whilst only incurring a small cost in terms of computational resources. We demonstrate our results on several datasets, showing that our method is robust in terms of variations in the layout and the number of residents in the house. Furthermore, we have incorporated the adjacency matrix into the CRF learning framework and have shown that it can improve performance over multiple baselines.

It can be tempting to think about smart homes like one thinks about smart cities. On the surface, smart homes and smart cities comprise coherent systems enabled by similar sensing and interactive technologies. It can also be argued that both are broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery. However, the home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world [37].

We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data, obviating the need for heuristic methods for choosing sparsity levels. This model can be solved efficiently using Variational Message Passing (VMP), which we have implemented in the Infer.NET framework for probabilistic programming and inference. We analyse the properties of the model via empirical validation on several accelerometer datasets. We provide source code to replicate all of the experiments in this paper.

In residential environments, floor maps, often required by location-based services, cannot be trivially acquired. Researchers have addressed the problem of automatic floor map construction in indoor environments using various modalities, such as inertial sensors, Radio Frequency (RF) fingerprinting and video cameras. Considering that some of these techniques are unavailable or impractical to implement in residential environments, in this paper, we focus on using RF signals to predict the number of walls between a wearable device and an access point. Using both supervised and unsupervised learning techniques on two data sets; a system-level data set of Bluetooth packets, and measurements on the signal attenuation, we construct wall prediction models that yield up to 91% identification rate. As a proof-of-concept, we also use the wall prediction models to infer the floor plan of a smart home deployment in a real residential environment.

Typically, when analysing patterns of activity in a smart home environment, the daily patterns of activity are either ignored completely or summarised into a high-level "hour-of-day" feature that is then combined with sensor activities. However, when summarising the temporal nature of an activity into a coarse feature such as this, not only is information lost after discretisation, but also the strength of the periodicity of the action is ignored. We propose to model the temporal nature of activities using circular statistics, and in particular by performing Bayesian inference with Wrapped Normal $$\mathcal {WN}$$ and $$\mathcal {WN}$$ Mixture $$\mathcal {WNM}$$ models. We firstly demonstrate the accuracy of inference on toy data using both Gibbs sampling and Expectation Propagation EP, and then show the results of the inference on publicly available smart-home data. Such models can be useful for analysis or prediction in their own right, or can be readily combined with larger models incorporating multiple modalities of sensor activity.

This paper presents an automated framework for the detection of the QRS complex from Electrocardiogram (ECG) signals. We introduce an artefact-tolerant pre-processing algorithm which emphasises a number of characteristics of the ECG that are representative of the QRS complex. With this processed ECG signal we train Logistic Regression and Support Vector Machine classification models. With our approach we obtain over 99.7% detection sensitivity and precision on the MIT-BIH database without using supplementary de-noising or pre-emphasis filters.

This paper investigates the fully automated computer-based detection of allergic reaction in oral food challenges using pediatric ECG signals. Nonallergic background is modeled using a mixture of Gaussians during oral food challenges, and the model likelihoods are used to determine whether a subject is allergic to a food type. The system performance is assessed on the dataset of 24 children (15 allergic and 9 nonallergic) totaling 34 h of data. The proposed detector correctly classified all nonallergic subjects (100 % specificity) and 12 allergic subjects (80 % sensitivity) and is capable of detecting allergy on average 17 min earlier than trained clinicians during oral food challenges, the gold standard of allergy diagnosis. Inclusion of the developed allergy classification platform during oral food challenges recorded would result in a 30% reduction of doses administered to allergic subjects. The results of study introduce the possibility to halt challenges earlier which can safely advance the state of clinical art of allergy diagnosis by reducing the overall exposure to the allergens.

In this paper, we tackle the problem of the food allergic detection in children, based on the analysis of the ECG signal. Through the detection of some changes of this signal, it is possible to detect any reaction before the tested subject experiments any physical reaction or any reaction that could be harmful to his/her life. To be able to realize this process in real-time and with portable devices, it is necessary to reduce the computational cost of the full process, from the ECG analysis to the allergy detection process.

This paper investigates the automated classification of oral food challenges ('allergy tests'). The electrocardiograms (ECG) of the subjects being tested for allergies were recorded via a wireless mote, and the QRS complexes were manually annotated and 18 features were extracted from the signals. Principal component analysis was used for feature decorelation and dimensionality reduction and diagonal covariance Gaussian mixture models were used to model non-reaction baseline patient condition. The generated subject independent log likelihood plots were used to separate allergic reaction by means of subject adaptive thresholding. The platform resulted in 87% accuracy of classification with 100% specificity. The algorithm presented can detect allergy up to 30 minutes sooner than the current state of the clinical art allergy detection (7minutes ± 9).

This paper details the process of oral food challenges (‘allergy tests’) and steps followed to investigate whether automatic classification of the tests is possible. It has been observed by trained staff that the mood and physiological signals of a subject being tested for allergies can change during the test if they are sensitive to the allergen they are being tested against. Data from thirteen subjects was recorded, and thirteen features were extracted from each of these datasets. The changes in the features were then analysed over the course of each test. It was noted that when a subject failed the challenge, some of the features extracted from the ECG trace changed suddenly near the time that the test was stopped. Threshold classification was employed, and ROC curves were generated. Some features gave rise to ROC areas of over 0.97 on certain subjects. An average ROC area of 0.57 was computed over all subjects and all features due to wide inter subject variability.

Many accelerometer-based energy expenditure estimation algorithms and platforms have been established in recent topical literature, and each boasts a high correlation against the gold standard in energy expenditure measurement, i.e. indirect calorimetry. The aim of this study was to implement a set of these algorithms, run them all over a common dataset and investigate the strengths and weaknesses associated with each. The algorithms were then ported to a SHIMMER device for a real time, mobile and non-invasive energy expenditure estimation solution. High correlations were found between the accelerometer-regressed energy expenditure estimates and the reference dataset both on a computer and SHIMMER-implementation of the algorithms.

There is no uniform approach in the literature for modelling sequential correlations in sequence classification problems. It is easy to find examples of unstructured models (e.g. logistic regression) where correlations are not taken into account at all, but there are also many examples where the correlations are explicitly incorporated into a – potentially computationally expensive – structured classification model (e.g. conditional random fields). In this paper we lay theoretical and empirical foundations for clarifying the types of problem which necessitate direct modelling of correlations in sequences, and the types of problem where unstructured models that capture sequential aspects solely through features are sufficient. The theoretical work in this paper shows that the rate of decay of auto-correlations within a sequence is related to the excess classification risk that is incurred by ignoring the structural aspect of the data. This is an intuitively appealing result, demonstrating the intimate link between the auto-correlations and excess classification risk. Drawing directly on this theory, we develop well-founded visual analytics tools that can be applied a priori on data sequences and we demonstrate how these tools can guide practitioners in specifying feature representations based on auto-correlation profiles. Empirical analysis is performed on three sequential datasets. With baseline feature templates, structured and unstructured models achieve similar performance, indicating no initial preference for either model. We then apply the visual analytics tools to the datasets, and show that classification performance in all cases is improved over baseline results when our tools are involved in defining feature representations.

There are at least two major challenges for machine learning when performing activity recognition in the smart-home setting. Firstly, the deployment context may be very different to the context in which learning occurs, due to both individual differences in typical activity patterns and different house and sensor layouts. Secondly, accurate labelling of training data is an extremely time-consuming process, and the resulting labels are potentially noisy and error-prone. We propose that these challenges are best solved by combining transfer learning and active learning, and argue that hierarchical Bayesian methods are particularly well suited to problems of this nature. We introduce a new active learning method that is based on on Bayesian model selection, and hence fits more concomitantly with the Bayesian framework than previous decision theoretic approaches, and is able to cope with situations that the simple but na¨ıve method of uncertainty sampling cannot. These initial results are promising and show the applicability of Bayesian model selection for active learning. We provide some experimental results combining two publicly available activity recognition from accelerometry data-sets, where we transfer from one data-set to another before performing active learning. This effectively utilises existing models to new domains where the parameters may be adapted to the new context if required. Here the results demonstrate that transfer learning is effective , and that the proposed evidence-based active selection method can be more effective than baseline methods for the subsequent active learning.

Obesity, depression, stroke, falls, cardiovascular and musculoskeletal disease are some of the biggest health issues and fastest-rising categories of healthcare costs. The associated expenditure is widely regarded as unsustainable and the impact on quality of life is felt by millions of people in the UK each day. The vision of the SPHERE IRC is not to develop fundamentally-new sensors for individual health conditions but rather to impact all these healthcare needs simultaneously through data-fusion and pattern-recognition from a common platform of non-medical/environmental sensors at home. The system will be general-purpose, low-cost and scalable. Sensors will be entirely passive, requiring no action by the user and hence suitable for all patients including the most vulnerable. A central hypothesis is that deviations from a user's established pattern of behaviour in their own home have particular, unexploited, diagnostic value.

Consider the situation where we have some pre-trained classification models for bike rental stations (or any other spatially located data). Given a new rental station (deployment context), we imagine that there might be some rental stations that are more similar to this station in terms of the daily usage patterns, whether or not these stations are close by or not. We propose to use a Gaussian Process (GP) to model the relationship between geographic location and the type of the station, as determined by heuristics based on the daily usage patterns. For a deployment station, we then find the closest stations in terms of the Gaussian Process (GP) function output, and then use the models trained on these stations on the deployment station. We compare against several baselines, and show that this method is able to outperform those baselines.

This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.

Modern wearable devices are equipped with increasingly powerful microcontrollers and therefore are increasingly capable of doing computationally heavy operations, such as feature extraction from sensor data. This paper quantifies the time and energy costs required for on-board computation of features on acceleration data, the reduction achieved in subsequent communication load compared with transmission of the raw data, and the impact on daily activity recognition in terms of classification accuracy. The results show that platforms based on modern 32-bit ARM Cortex-M microcontrollers significantly benefit from on-board extraction of time-domain features. On the other hand, efficiency gains from computation of frequency domain features at the moment largely remain out of their reach.

This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter, thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.