Activities Of Daily Living Use Research Articles

Lack of Methodological Rigor for Task-Based Functional Magnetic Resonance Imaging: Injury-Related Fear or Failure to Correct?

Lack of Methodological Rigor for Task-Based Functional Magnetic Resonance Imaging: Injury-Related Fear or Failure to Correct?

New Insights into Activities of Daily Living Performance in Chronic Obstructive Pulmonary Disease.

PurposeThere have been calls for more knowledge of activities of daily living (ADL) performance in order to address interventions in pulmonary rehabilitation effectively. Everyday technology (ET) has become an integrated dimension of ADL, impacting the ways in which ADL is performed. To improve everyday functioning and quality of life, the use of ADL and ET use needs to be evaluated and addressed effectively in interventions. Therefore, the aim of this study was twofold: 1) to explore the quality of ADL performance, and 2) to investigate the relationship between observation and self-reported ADL performance and ability to use everyday technologies in people living with COPD.MethodsThis cross-sectional study involved 84 participants aged 46–87 years. Participants were recruited through healthcare centres in the Northern Region of Denmark using a convenience sampling procedure. Data were collected using standardized assessments that investigated different ADL perspectives: self-reported ADL tasks and ET use, observed motor and process ability, and need for assistance. Data were analysed and presented using descriptive statistics and Pearson’s correlation coefficient.ResultsThe most affected ADL tasks were mobility within or outside the home, lower dressing, bathing, pedicuring, cooking, shopping, cleaning and washing clothes. New insights into the quality of ADL performance in people living with COPD were presented in terms of detailed ADL motor skills and ADL process skills, as well as the predicted need for support to function in the community. Moreover, new insights into the relationship between observation and self-reported ADL performance (r=0.546, p<0.01; r=0.297, p<0.01) and between ADL performance and self-perceived ability to use ET (r=0.524, p<0.01; r=0.273, p<0.05; r=0.044, p=0.692) were presented.ConclusionOverall, the knowledge from the present study is valuable for focusing interventions that address challenging ADL performance and ET use through relevant and realistic activities. The ability to use ET is important to evaluate and target pulmonary rehabilitation.

An Activity Rule Based Approach to Simulate ADL Sequences

The concept of activities of daily living (ADL) has for many years successfully been used in a broad range of health and health care applications. Recent hardware and software developments suggest that the future use of ADL will not only benefit from the transition from manually created ADL logs to automatic sensor-based activity recognition and logging but also from the transition from manual inspection of ADL sequences to their automatic software-driven analysis. This ADL sequence analysis software will be core part in mission critical systems, like ambient assisted living, to detect for example changing health status. Therefore, proper testing and evaluation of this software is mandatory before its deployment. However, testing requires data sets that include normal ADL sequences, hazards, and various kinds of long term behavioral changes; which means it might require weeks or even months to monitor individuals to capture such ADL sequences. Thus, collecting such data sets is very costly, if feasible at all; and very few data sets are available on-line. Therefore, we present an approach to create the necessary data sets for testing through simulation. The simulation of ADL sequences is based on existing ADL sequences and uses probabilistic activity instigation and durations with a novel concept called activity rules to create data sets for proper testing. Activity rules are used to model how individuals resolve activity conflicts. We implemented these concepts as a discrete event simulator, called ADLSim. The evaluation of ADLsim shows that the simulated ADL sequences are realistic and able to capture the variability and non-predictable behavior found in the real world, and that activity rules can impact simulation results significantly.

Co-adaptation of assistive mobility devices and residential functions

Purpose For facilitating an independent living for elderly or disabled people, a method has been developed at TUM Building Realization and Robotics Lab, which aims at creating intelligent living environments by adjusting mobility systems and everyday objects (e.g. appliances) as modular and complementary subsystems. The proposed system comprises an intelligent wheelchair, which enables simplified use of the bath-room, kitchen and living room, etc. It can dock at certain functions, e.g. the toilet, both on a physical and information technology level. Method Studies show that the major problem for people with physical and/ or cognitive disabilities in everyday routine and in managing the Activities of Daily Living (ADLs) is the surmounting of different height levels and distances. Even for a person with a good physical fitness, it is extremely difficult or even impossible to transfer from a wheelchair onto a couch autonomously. The reason is the height difference between the furniture and wheelchair, as well as the space or gap that occurs mostly between the transport device and the piece of furniture. The proposed work presents a thorough research study, which enabled a huge gain in knowledge, concerning the re-design of household appliances and devices, in order for them to be straightforwardly and cost effectively integrated with mobility supporting robots. The outcome of this research, led to the participation on a research project which involves 7 companies and 3 universities. In order to realize the co-adaptation of an assistive mobility device and residential functions, the following development approach steps were followed: i) research and identification of needs, ii) definition of requirements, iii) identification of technologies and processes, iv) initial concept, v) experiment in real environment, vi) final concept and further development roadmap. Results & Discussion Most wheelchairs are designed with a fixed height and do not provide features for altering their elevation level. Additionally, handicapped individuals face major problems when reaching items located higher, due to the fact that objects in the environment are mostly located statically, and it is difficult to get at them as a disabled user. Especially for disabled people, the functions should come to them instead of the other way around. The results of the study prove that the coadaptation of assistive mobility devices with various residential functions, allow the complexity reduction of mobility robots. Companies notice a huge potential in such an approach, as it would allow them to design less complex, and thus more cost effective robots for ADLs use. The high cost of robots is currently one of the main obstacles for deploying them in the home environment. Based on this study, TUM was finally able to set up a government funded 3-year R&D project (total funding cost: 3.9 Million €, starting from June 2012) where major strategies, processes and components necessary to achieve uninterrupted mobility chains for elderly people will be brought to product market level.

A Knowledge-Driven Approach to Activity Recognition in Smart Homes

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.