Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

The effect of market and learning orientation on strategy dynamics : The contributing effect of organisational change capability

McGuinness, T. and Morgan, R. (2005). The effect of market and learning orientation on strategy dynamics: The contributing effect of organisational change capability. European Journal of Marketing. 39(11-12), pp.1306-1326 RAE2008

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Organizational learning orientation - how can it foster alliance relationships?

Purpose – The purpose of this paper is to examine the importance of an organization's learning orientation in developing alliance relationships.

Design/methodology/approach – The paper is based on research conducted on organizations involved in alliances as well as practical evidence.

Findings – The paper finds that alliance managers need to focus on developing learning capabilities within their organizations to achieve superior alliance performance.

Practical implications – The paper has practical implications for managers involved in alliances as it shows the importance of an organization focusing on learning. With the relatively small number of alliance relationships that succeed, it is important that managers harness their organization's learning potential in the context of an alliance.

Originality/value – The paper is unique as it highlights the importance of an organization's learning orientation in an alliance context.

Firm-level strategic orientation and performance: a synthesis and extension of the knowledge base on entrepreneurial, market and learning orientation

Should a firm stay focused or should it rather adopt a broader strategic perspective? This dissertation summarizes and extends the existing knowledge base on entrepreneurial, market, and learning orientation. Building on multiple theoretical perspectives, empirical evidence from prior studies, as well as on survey and archival data collected in two economic contexts, performance effects from individual orientations, their dimensions and combinations are explored. Results reveal that the three strategic orientations are highly interrelated and that their relationship to firm performance is more complex than previously assumed.

Learning orientation and market orientation: From an alliance perspective

The literature on market orientation has grown in significance during the past decade. Whilst there is more literature on market orientation, the literature on learning orientation is also starting to increase. Numerous researchers have studied and found support for the positive relationship between market orientation and learning orientation (eg Slater and Narver 1995). However, not many researchers have investigated the relationship that firms with a high learning orientation have a high market orientation (eg Sinkula, Baker and Noordewier 1997). This paper attempts to investigate that link in more detail. An alliance perspective is taken in understanding this relationship.

Organizational learning orientation profile and implications for strategic planning capacity

Theory development on the relationship between strategic planning and organizational performance has focussed on largely discrete examinations of dependent and independent variables. While the literature has examined the impact of organizational learning on strategic planning, no holistic empirical approaches have been employed in order to fully explore the inter-play between these important constructs. This paper addresses the cited limitations in both the strategic planning and organizational performance literatures by creating profiles of organizational learning and strategic planning capacity using a configuration theory-based approach. The organizational learning orientation profiles (OLOPs) created of prospector, disseminator, interpretative and memory, contribute to theory development regarding the relationship of strategic planning and organizational learning. The theory developed provides insights that have not been previously reported.

Sentidos e significados do conceito de divisão provenientes de Atividade Orientadora de Ensino

Pós-graduação em Docência para a Educação Básica - FC