Intelligent wheelchairs can help increase independent mobility for elderly residents with cognitive impairment, who are currently excluded from the use of powered wheelchairs. This paper presents three case studies, demonstrating the efficacy of the NOAH (Navigation and Obstacle Avoidance Help) system. The findings reported can be used to refine our understanding of user needs and help identify methods to improve the quality of life of the intended users.

While machine learning methods have proven to be a highly valuable tool in solving numerous problems in assistive technology,state-of-the-art machine learning algorithms and corresponding results are not always accessible to assistive technology researchers due to required domain knowledge and complicated model parameters. This work explores the use of recent work in machine learning to entirely automate the machine learning pipeline, from feature extraction to classification. A nonparametrically guided autoencoder is used toextract features and perform classification while Bayesian optimization is used to automatically tune the parameters of the model for best performance. Empirical analysis is performed on a real-world rehabilitation research problem. The entirely automated approach significantly outperforms previously published results using carefully tuned machine learning algorithms on the same data.

Wandering is a problematic behavior that is common among people with dementia (PwD), and is highly influenced by the elders’ background and by contextual factors specific to the situation. We have developed the Ambient Augmented Memory System (AAMS) to support the caregiver to implement interventions based on providing external memory aids to the PwD. To provide a successful intervention, it is required to use an individualized approach that considers the context of the PwD situation. To reach this end, we extended the AAMS system to include an ontological model to support the context-aware tailoring of interventions for wandering. In this paper, we illustrate the ontology flexibility to personalize the AAMS system to support direct and indirect interventions for wandering through mobile devices.

Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring.The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-one-person-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features.Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring.These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.

Falls are a major problem for older adults. A continuous unobtrusive in-home monitoring system that provides an accurate automated assessment of fall risk and detects when falls have occurred would allow for timely intervention and prevention allowing individual to remain healthier and independent longer. Sensor networks have been installed in apartments of older adult volunteers at TigerPlace, an independent senior living community. Initial results comparing gait parameters captured with a Microsoft Kinect with ground truth clinical fall risk assessments and GAITRite data are presented.

In this paper we discuss the gap that exists between the caregivers of older adults attempting to age-in-place and sophisticated ”smart-home” systems that can sense the environment and provide assistance when needed. We argue that smart-home systems need to be customizable by end-users, and we present a general-purpose model for cognitive assistive technology that can be adapted to suit many different tasks, users and environments. Al- though we can provide mechanisms for engineers and designers to build and adapt smart-home systems based on this general-purpose model, these mechanisms are not easily understood by or sufficiently user-friendly for actual end users such as older adults and their care- givers. Our goal is therefore to study how to bridge the gap between the end-users and this technology. In this paper, we discuss our work on this problem from both sides: developing technology that is customizable and general-purpose, and studying user’s abilities and needs when it comes to building smart-home systems to help with activities of daily living. We show how a large gap still exists, and propose ideas for how to bridge the gap.

This research is a discriminative analysis of conversational dialogs involving individuals suffering from dementia of Alzheimer’s type. Several metric analyses are applied to the transcripts of the Carolina Conversation Corpus (Pope and Davis 2011) in order to determine if there are significant statistical differences between individuals with and without Alzheimer’s disease. Results from the analysis indicate that go-ahead utterances, certain fluency measures, and paraphrasing provide defensible means of differentiating the linguistic characteristics of spontaneous speech between healthy individuals and those with Alzheimer’s disease. Several machine learning algorithms were used to classify the speech of individuals with and without dementia of the Alzheimer’s type.

The aging population, the increasing cost of formal health care, caregiver burden and the importance that older adults place on living independently in their own homes motivate the need for the development of patient-centric technologies that promote safe independent living. These patient-centric technologies need to address various aging related physical and cognitive health problems such as heart disease, diabetes, deterioration of physical function, falling, wandering, strokes, and memory problems, lack of medication adher- ence, cognitive decline and loneliness. Advances in the sensor and computing technology that allow for ambient unobtrusive and continuous home monitoring have opened new vistas for the development of such technologies.

DISCUSSION: LATENT VARIABLE GRAPHICAL MODEL SELECTION VIA CONVEX OPTIMIZATION By Emmanuel J. Candés and Mahdi Soltanolkotabi Stanford University We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements. Clearly, the methodology introduced in this paper is of potential interest across many disciplines. In the following, we will first discuss this paper in more detail and then reflect on the versatility of the low-rank sparse decomposition. The proposed scheme is an extension of the graphical lasso of Yuan and Lin [15] (see also [1, 6]), which is a popular approach for learning the structure in an undirected Gaussian graphical model.

Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be solved effectively by a lightweight but powerful approach, consisting of: (1) the conversion of any traditional one-parameter crisp regression model into a two-parameter soft regression model, seen as a normal conditional density estimator, by the use of newly-introduced enrichment methods; and (2) the reframing of an enriched soft regression model to new contexts by an instance-dependent optimisation of the expected loss derived from the conditional normal distribution.