People, sensors, decisions

Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations

This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.

A preliminary evaluation of a client-centred prompting tool for supporting everyday activities in individuals with mild to moderate levels of cognitive impairment due to dementia

Objectives

Technology-based prompting has the potential to support people with dementia to complete multistep tasks in the home. However, these devices can be complex to use. This paper reports a feasibility trial of a personalised touchscreen digital prompter designed for home use.

Methodology: A tablet-based prompter suitable for people living with dementia was developed, along with a detailed guidance manual. Carers loaded instructions for completing the task onto the prompter, and the person with dementia then used the tablet independently to complete a task. Eleven couples used the prompter ‘out-of-the-box’ with no support other than a guidance manual.

Results

The majority of participants with dementia could follow the steps on the prompter, and carers were able to breakdown and load tasks onto the prompter. Eight couples used the prompter successfully to complete goals that they had identified in advance. These included preparing simple snacks and using a TV remote control. Successfully achieving goals was associated with more frequent use of the prompting screen on more days, but not higher levels of editing or previewing of tasks.

Conclusion

The study provides the preliminary evidence that family caregivers can use a touchscreen tablet, software and manual package to identify specific tasks and break these down into steps and that people living with dementia can then follow the prompts to complete the tasks. This potentially represents an important advance in dementia care. Further testing is required to establish efficacy and to identify any factors that impact on outcomes.

Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats

Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

Energy Efficient Execution of POMDP Policies

Recent advances in planning techniques for partially observable Markov decision processes (POMDPs) have focused on online search techniques and offline point-based value iteration. While these techniques allow practitioners to obtain policies for fairly large problems, they assume that a nonnegligible amount of computation can be done between each decision point. In contrast, the recent proliferation of mobile and embedded devices has lead to a surge of applications that could benefit from state-of-the-art planning techniques if they can operate under severe constraints on computational resources. To that effect, we describe two techniques to compile policies into controllers that can be executed by a mere table lookup at each decision point. The first approach compiles policies induced by a set of alpha vectors (such as those obtained by point-based techniques) into approximately equivalent controllers, while the second approach performs a simulation to compile arbitrary policies into approximately equivalent controllers. We also describe an approach to compress controllers by removing redundant and dominated nodes, often yielding smaller and yet better controllers. Further compression and higher value can sometimes be obtained by considering stochastic controllers. The compilation and compression techniques are demonstrated on benchmark problems as well as a mobile application to help persons with Alzheimer's to way-find. The battery consumption of several POMDP policies is compared against finite-state controllers learned using methods introduced in this paper. Experiments performed on the Nexus 4 phone show that finite-state controllers are the least battery consuming POMDP policies.

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.