Advances in the science of objective physical activity monitoring : 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement

Editorial: This theme issue of BJSM presents key papers from the 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM). The July 2013 conference was hosted by the University of Massachusetts and was attended by researchers, clinicians, students and technology vendors for North America, Europe, Australasia and Asia...

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Human activity-induced vibration in slender structural systems

Human activity-induced vibrations in slender structural sys tems become apparent in many different excitation modes and consequent action effects that cause discomfort to occupants, crowd panic and damage to public infrastructure. Resulting loss of public confidence in safety of structures, economic losses, cost of retrofit and repairs can be significant. Advanced computational and visualisation techniques enable engineers and architects to evolve bold and innovative structural forms, very often without precedence. New composite and hybrid materials that are making their presence in structural systems lack historical evidence of satisfactory performance over anticipated design life. These structural systems are susceptible to multi-modal and coupled excitation that are very complex and have inadequate design guidance in the present codes and good practice guides. Many incidents of amplified resonant response have been reported in buildings, footbridges, stadia a nd other crowded structures with adverse consequences. As a result, attenuation of human-induced vibration of innovative and slender structural systems very ofte n requires special studies during the design process. Dynamic activities possess variable characteristics and thereby induce complex responses in structures that are sensitive to parametric variations. Rigorous analytical techniques are available for investigation of such complex actions and responses to produce acceptable performance in structural systems. This paper presents an overview and a critique of existing code provisions for human-induced vibration followed by studies on the performance of three contrasting structural systems that exhibit complex vibration. The dynamic responses of these systems under human-induced vibrations have been carried out using experimentally validated computer simulation techniques. The outcomes of these studies will have engineering applications for safe and sustainable structures and a basis for developing design guidance.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Peat, pine stumps and people: interactions behind climate, vegetation change and human activity in wetland archaeology at Loch Farlary, Northern Scotland

First paragraph: In 1993, a peat-cutter, Bruce Field, working on the blanket peat bank he rented from the Sutherland Estate by Loch Farlary, above Golspie in Sutherland (fig 1), reported to Scottish Natural Heritage and Historic Scotland several pieces of pine wood bearing axe marks. Their depth in the peat suggested the cut marks to be prehistoric. This paper summarizes the work undertaken to understand the age and archaeological significance of this find (see also Tipping et al 2001 in press). The pine trees were initially thought to be part of a population that flourished briefly across northern Scotland in the middle of the Holocene period from c 4800 cal BP (Huntley, Daniell & Allen 1997). The subsequent collapse across northernmost Scotland of this population, the pine decline, at around 4200-4000 cal BP is unexplained: climate change has been widely assumed (Dubois & Ferguson 1985; Bridge, Haggart & Lowe 1990; Gear & Huntley 1991) but anthropogenic activity has not been disproved (Birks 1975; Bennett 1995). It was hypothesized that the Farlary find would allow for the first time the direct link between human woodland clearance and the Early Bronze Age pine decline.

Early Holocene vegetation, human activity and climate from Sarawak, Malaysian Borneo

A 40mcore from Loagan Bunut,Malaysian Borneo, yielded a high-resolution early Holocene (11.3e6.75 ka) sequence of marginal-marine deposits. Palynological analysis showed relatively stable fire-regulated lowland forest through this time, with the local development and regression of mangrove vegetation. A general trend of rising rainfall and thus strengthening North East monsoonal circulation linked to the migration of the mean position of the ICTZ was interrupted by what may be episodes of drier climate and weakening monsoonal activity at 9250-8890, 7900 and 7600-7545 cal. BP. Magnetic susceptibility peaks suggestmarked short-term ENSO-style activity superimposed upon this record. Repeated markers for openand disturbed habitats, plus occasional imported and probably-cultivated taxa, point towards human impact from the earliest Holocene on the wet tropical forest at Loagan Bunut.