Identification of activities of daily living in tremorous patients using inertial sensors

BACKGROUNDMuch attention has been given to the use of inertial sensors for remote monitoring of individuals suffering from neurological pathologies. However, the focus has been mostly on the detection of symptoms like tremor or dyskinesia, not on identifying specific activities carried out by subjects. The objective of this study was to develop an automated segmentation and recognition methodology, from inertial sensor data, to identify tasks and motor patterns during activities of daily living. This will enable clinicians to contextualize the symptoms of these diseases and improve their treatment.METHODSWe designed and tested a methodology to automatically label continuous upper-limb activity from IMUs (Inertial Measurement Units). Three classification problems are considered: the task itself, the precision level required by the task with respect to movement (fine or gross) and the trajectory of the task (distal or proximal). These problems were identified by tremor experts as clinically important aspects to be monitored in tremor patients while performing daily activities. The proposed methodology reveals the relation between the functional context or activity and the patient's on-going tremor to clinicians.RESULTSOverall task identification rate was 86%. Task precision and task trajectory are classified with 79% and 89% accuracy, respectively. Aligning the semantic nature of the activity with the tremor location and intensity can also provide novel and relevant information for clinical monitoring of tremor and help clinicians and researchers as a useful tool to develop new therapies or strategies and novel anti-tremor medications to combat context dependent tremor.CONCLUSIONSThe present study describes the development of a comprehensive methodology based on machine learning techniques to segment and detect activities of daily living in people with tremor using inertial sensors, which aims at facilitating detailed interpretation of tremor movements by neurologists.     

Mobile phone use among college students is a sedentary leisure behavior which may interfere with exercise

Research suggests that cell phone use is related to sedentary behavior, that cell phone use during exercise reduces intensity, and that high frequency cell phone users are less fit than other users. Thus, cell phone use appears connected to health and fitness behaviors and should be better understood within this context. The present study investigated the sedentary nature of cell phone use, and examined the likelihood of cellphone use interfering with exercise behavior.DesignA validated survey was administered to a random sample of students from a public US university (N = 226).ResultsMean self-reported cell phone use was 380 min day⁻¹, 87% reported cell phone use primarily occurs while sitting, and 70% of use was for leisure. Cell phone use was positively related to sedentary behavior (β = 0.30, p < 0.001). It was not related to physical activity. However, the likelihood of cell phone use during moderate (p = 0.006) and mild (p < 0.001) intensity exercise increased as cell phone use increased.ConclusionLike other screens (e.g., TVs), cell phone use appears to be a sedentary leisure behavior. Furthermore, high frequency use increases the likelihood that it will occur during exercise, likely lowering exercise intensity.     

Medium-term effects of a two-desk sit/stand workstation on cognitive performance and workload for healthy people performing sedentary work: a secondary analysis of a randomised controlled trial

Implementing sit/stand workstations in sedentary work environments is a common way to reduce sedentary time, but their medium-term effect on cognitive performance is unclear. To address this circumstance, eighteen office workers participated in a two-arm, randomised controlled cross-over trial (ClinicalTrials.gov Identifier: NCT02825303), either working at a traditional (sit) or an interventional (sit/stand) workplace for 23 weeks. Cognitive performance (working speed, reaction time, concentration performance, accuracy), workload and relevant covariates (salivary cortisol level, heart rate, physical activity, sitting time) were measured pre- and post-intervention under laboratory conditions. MANOVA and RMANOVA results did not show differences in performance parameters and workload, respectively, between sit/stand and traditional workplace users. Differences in text editing accuracy and cortisol levels for sit/stand workstation users indicate potential connectivity to cognitive parameters which should be further examined with large-scale studies. Practitioner summary: Medium-term effects of working at sit/stand workstations on cognitive performance and workload are unexplored. This randomised controlled trial suggests that cognitive performance and workload are unaffected for sit/stand workstation users after 23?weeks of use. However, accuracy appeared to improve and physiological stress appeared to be altered.

Abbreviations: BMI: body mass index; IPAQ: International physical activity questionnaire; MET: metabolic equivalent of task; MANOVA: multivariate ANOVA; NASA TLX: NASA task load index; RMANOVA: repeated measures ANOVA     

DynamiCITY: Revealing city dynamics from citizens social media broadcasts

Social media and mobile devices have revolutionized the way people communicate and share information in various contexts, such as in cities. In today’s “smart” cities, massive amounts of multiple forms of geolocated content is generated daily in social media, out of which knowledge for social interactions and urban dynamics can be derived. This work addresses the problem of detecting urban social activity patterns and interactions, by modeling cities into “dynamic areas”, i.e., coherent geographic areas shaped through social activities. Social media users provide the information on such social activities and interactions in cases when they are on the move around the city neighborhoods. The proposed approach models city places as feature vectors which represent users visiting patterns (social activity), the time of observed visits (temporal activity), and the context of functionality of visited places category. To uncover the dynamics of city areas, a clustering approach is proposed which considers the derived feature vectors to group people’s activities with respect to location, time, and context. The proposed methodology has been implemented on the DynamiCITY platform which demonstrates neighborhood analytics via a Web interface that allows end-users to explore neighborhoods dynamics and gain insights for city cross-neighborhood patterns and inter-relationships.     

Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results

In recent decades, decreasing physical activity has emerged as one of the major issues affecting human health since people increasingly engaged in sedentary behavior in their homes and workplaces. In physical activity research, using GPS trajectories and advanced GIS methods has a potential for greatly enhancing our understanding of the association between objectively measured moderate and vigorous physical activity and physical and social environments. Relying only on objectively measured physical activity intensity, however, ignores the role of different places and types of physical activity on people's health outcomes. The aim of this study is to propose an approach to classifying physical activity in free-living conditions for physical activity research using published smartphone accelerometer data. Random forest and gradient boosting are used to predict jogging, walking, sitting, and standing. Generated training models based on the two classifiers are tested on accelerometer data collected from the smartphones of two subjects in free-living conditions. GPS trajectories with predicted physical activity labels are visually explored on a map to offer new insight on the assessment of the predicted results of daily activities and the identification of any difference in the results between random forest and gradient boosting. The findings of this study indicate that random forest and gradient boosting enable accurate physical activity classification in free-living conditions. GPS trajectories linked with predicted labels on a map assist the visual exploration of the erroneous prediction in daily activities including in-vehicle activities.     

Sedentary behaviour and health at work: an investigation of industrial sector,job role,gender and geographical differences

This article presents baseline data from 1120 employees across 10 worksites enrolled in a workplace physical activity intervention. The study provides new data on physical activity, sedentary behaviour, and health and highlights gender, geographical, job type and industrial sector differences. Sitting at work accounted for more than 60% of participants’ total daily sitting time on work days. Weekly and monthly hours worked, body mass index (BMI) and waist circumference were significantly higher for workers in the private sector compared to the public sector. Employees in sales and customer services had significantly higher BMI scores and significantly lower scores for workability index (WAI), job satisfaction, organisational commitment and job motivation, compared to other groups. This study provides further evidence that work is a major contributor to sedentary behaviour and supports the pressing need for interventions particularly targeting private sector industries and sales and customer service sectors.

Practitioner Summary: Work accounts for more than 60% of the daily sitting time. Private sector employees had higher BMIs than those in the public sector and employees in sales and customer services had higher BMIs and poorer health compared to other occupations, suggesting that these groups should be targeted in workplace interventions.     

Stairstep recognition and counting in a serious Game for increasing users’ physical activity

The high diffusion of smartphones in the users’ pockets allows to sense their movements, thus monitoring the amount of physical activity they do during the day. But, it also gives the possibility to use these devices to persuade people to change their behaviors. In this paper, we present ClimbTheWorld, a serious game which uses a machine learning-based technique to recognize and count stairsteps and aims at persuading people to use stairs instead of elevators or escalators. We perform a fine-grained analysis by exploiting smartphone sensors to recognize single stairsteps. Energy consumption is widely investigated to avoid exhausting smartphone battery. Moreover, we present game appreciation and persuasive power results after a trial experiment with 13 participants.     

Toward a persuasive mobile application to reduce sedentary behavior

Prolonged sitting is a potential health risk, not only for people with an inactive lifestyle but also for those who meet the daily physical activity recommendations. Mobile applications that trigger people to take regular breaks from sitting seem promising. In this paper, we present the results of our quest to create effective persuasive mobile applications aimed at reducing sedentary behavior. First, we developed SitCoach, a mobile application to nudge office workers from their seats. SitCoach monitors physical activity and sedentary behavior and provides timely persuasive messages suggesting active breaks. A user test showed that users had little awareness of the risks of prolonged sitting and considered their ability to take active breaks to be highly dependent on external factors. The results from this study formed the basis for a second experiment, which was more extensive in duration and number of participants. In this 6-week experiment, office workers received timely persuasive messages on their smart phones, advising them to take an active break whenever they were sitting behind their computer for too long. Compared to a Control group who did not receive these messages, a significant decrease in computer activity was achieved. The studies show the potential and limitations of using a smart phone as a platform for reducing sedentary behavior. We conclude with recommendations to create effective mobile applications that motivate people to take regular breaks from sitting.     

Understanding the popular users: Following,affiliation influence and leadership on GitHub

Context: the ability to follow other users and projects on GitHub has introduced a new layer of open source software development participants who observe but do not contribute to projects. It has not been fully explored how following others influences the actions of GitHub users. Objective: this paper studies the motivation behind following (or not following) others and the influence of popular users on their followers. Method: a mixed methods research approach was used including a survey of 800 GitHub users to uncover the reasons for following on GitHub and a complementary quantitative analysis of the activity of GitHub users to examine influence. Our quantitative analysis studied 199 popular (most followed) users and their followers. Results: we found that popular users do influence their followers by guiding them to new projects. As a user’s popularity increases, so does their rate of influence, yet the same is not true for a popular user’s rate of contribution. Conclusions: these results indicate that a new type of leadership is emerging through GitHub’s following feature and popularity can be more important than contribution in influencing others. We discuss implications of popularity and influence and their impact on social structure and leadership on OSS projects.     

Walking Works Wonders: a tailored workplace intervention evaluated over 24 months

This article presents longitudinal data from 1120 participants across 10 worksites enrolled in Walking Works Wonders, a tailored intervention designed to increase physical activity and reduce sedentary behaviour. The intervention was evaluated over 2 years, using a quasi-experimental design comprising 3 conditions: tailored information; standard information and control. This study explored the impact of the intervention on objective measures (BMI, %Fat, waist circumference, blood pressure and heart rate) and self-reported measures of physical activity, sedentary behaviour, physical and psychological health. Interventions tailored to employees’ stage of change significantly reduced BMI and waist circumference compared to standard and control conditions. Employees who received either a standard or tailored intervention demonstrated significantly higher work ability, organizational commitment, job motivation, job satisfaction and a reduction in intention to quit the organization. The results suggest that adopting a tailored approach to interventions is particularly effective in terms of improving health in the workplace.

Practitioner Summary: This study describes Walking Works Wonders, a tailored intervention, which aims to encourage physical activity in the workplace. The study evaluated Walking Works Wonders over a 2 year period and demonstrated that interventions are more effective in improving health outcomes where the information is tailored to employees’ stage of change.     

Crowds,citizens and sensors: process and practice for mobilising learning

Participatory sensing is an emerging field in which citizens are empowered by technologies to monitor their own environments. Harvesting and analysing data gathered in response to personal or local enquiries can be seen as an antidote to information provided by official sources. Democratising sensing means that ordinary people can learn about and understand the world around them better and can be a part of the decision-making in improving environments for all. In this paper, we review and describe participatory sensing and discuss this in relation to making a series of prototype tools and applications for mobile users—Located Lexicon, Where’s Fenton? and Tall Buildings. In the first of these projects, Located Lexicon, we wanted to find out whether a lexicon of terms derived from user-generated content could enable the formation of Twitter like groups that allow users to engage in finding out more about their location. In the second project, Where’s Fenton? we made a publicly available app that involves users in counting the abundance and logging the location of deer in a park. This project focused specifically on anonymity of the user in collecting data for a specific enquiry. In the last project, Tall Buildings, we experimented with using dimensions of altitude, distance and speed to encourage users to physically explore a city from its rooftops. In all of these projects, we experiment with the pedestrian as a human sensor and the methods and roles they may engage in to make new discoveries. The underlying premise for our work is that it is not possible to calibrate people to be identical, so experimenting with crowd-sourced data opens up thinking about the way we observe and learn about the physical environment.     

A collaborative user-centered framework for recommending items in Online Social Networks

Recommender Systems are more and more playing an important role in our life, representing useful tools helping users to find “what they need” from a very large number of candidates and supporting people in making decisions in various contexts: what items to buy, which movie to watch, or even who they can invite to their social network, etc. In this paper, we propose a novel collaborative user-centered recommendation approach in which several aspects related to users and available in Online Social Networks – i.e. preferences (usually in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a sentiment), behavior (in the majority of cases logs of past items’ observations made by users), feedbacks (usually expressed in the form of ratings) – are considered and integrated together with items’ features and context information within a general framework that can support different applications using proper customizations (e.g., recommendation of news, photos, movies, travels, etc.). Experiments on system accuracy and user satisfaction in several domains shows how our approach provides very promising and interesting results.     

Modeling patterns of activities using activity curves

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.     

Utilization of user feedback in indoor positioning system

We propose an interpolation-based fingerprinting technique utilizing user feedback which does not require an exhaustive training phase typically seen in the indoor localization solutions. We argue that the contribution of users’ feedback to any positioning system is two-fold. Firstly, users’ feedback greatly help in fine-tuning an under-trained positioning system with proper filtering. Secondly, if users are well-behaved, our experimental results show that the participation of end-users can actually assist in the construction of a positioning system incrementally from scratch. We also show that user feedback-based positioning system adapts quite well when surroundings change. Our present system is built upon Bluetooth.     

Resource virtualization methodology for on-demand allocation in cloud computing systems

The resources’ heterogeneity and unbalanced capability, together with the diversity of resource requirements in cloud computing systems, have produced great contradictions between resources’ tight coupling characteristics and user’s multi-granularities requirements. We propose a resource virtualization model and its on-demand allocation oriented infrastructure mainly providing computing services to solve that problem. A loosely coupled resource environment centered on resource users is created to complete a mapping from physical view of resources to logic view of resources. Heuristic resource combination algorithm (HRCA) is proposed to transform physical resources to logic resources, which meets two requirements: randomness in combination and fluctuation control to the size of resources granularities. On the basis of the appraisal indexes presented for the on-demand allocation, resource matching algorithm (RMA), targeting at resource satisfaction with the highest resource utilization, is designed to reuse resources. RMA can satisfy users’ requirement in limited time and keep resource satisfaction in the highest level in the condition of logic resources granularities being less than their required size. Resource reconfiguration algorithm (RRA) is presented to implement resource matching in the condition that virtual computing resource pool cannot match granularities of resource requirements. RRA assures the lowest resource refusal rate and the greatest resource satisfaction. We verify the effectiveness, performance and accuracy of algorithms in implementing the goal of resource virtualization centered on resource users and on-demand allocation.     

A multi-functional mobility assist device for sit-to-stand motion

This paper presents a multi-functional Sit-to-Stand (STS) assistive device¹, which seeks to improve the mobility of individuals with lower extremity weaknesses. While originally designed for a person suffering from post-polio syndrome who could not rise from a seated position unassisted, the device found its utility among overweight and elderly persons and physical therapists. There is a growing population aged 65 and up for whom this essential activity of daily life can be a painful task. Lack of availability of fully-assistive Sit-to-Stand devices, which provide the STS motion and also aid in ambulation presents an unmet need for such individuals. In this paper, we present a novel 6-bar Sit-to-Stand (STS) linkage mechanism integrated into the device, which follows the motion trajectory of the hip joint and provides a comfortable orientation of the supporting bar during the motion. The device is positioned both as a functional assistive device meant for end-users constrained to stay at home due to their disability as well as an ambulation therapy aid to assist physical therapists and care providers to help them lift patients. The device, during the development stage, was tested at several assisted living facilities and nursing homes and is currently being used nationwide.     

Evaluation of an Electronic App Developed to Assist Clinicians in the Management of Behavioral and Psychological Symptoms of Dementia (BPSD)

Behavioral and psychological symptoms in people with dementia (BPSD) are ubiquitous, but their management is suboptimal. More readily available evidence-based information for clinicians could assist in improving care. While technological advances now make such information available, evidence for their utility and acceptance is limited. The utility of an Application (App) was evaluated to support clinicians managing BPSD. Evaluation comprised qualitative feedback from senior Australian clinicians and worldwide user analytic data. Analytic data included numbers of active users, their demographics, and details of user sessions. Focus groups were conducted regarding accessibility, usability, and usefulness of the App. Clinicians reported that the App was useful in supporting them to care for persons presenting with BPSD, by providing ready access to information. Over time uptake worldwide steadily increased. The BPSD Guide App has the capacity to provide clinicians with access to evidence-based information. Outcomes will inform future development and updates.     

Embodied cognition of information visualization: Human–computer interaction with six degrees of freedom in movement of an information space

Mental healthcare has been identified as one of the major social and economic challenges that society will face in the coming decades. In this paper, we explore the potential of technology to improve people’s access to and engagement in, as well as the effectiveness and affordability of, mental healthcare services while interacting with computers. Information visualization plays a crucial role in people’s interaction with computers. Specifically, information visualization represents data or concepts graphically and helps people construct cognitive maps i.e., mental representations of the information space. Well-designed information visualization methods enable users to employ their mental capabilities to manipulate an information space and perceive it based on good mental health. According to the latest study of mental capability, movement will guide insight in problem solving situations. Movement in the 3D world is a crucial part of our mental capability, but traditional information visualization approaches fail to consider it. In an attempt to fill this research gap, we propose a new information visualization method that integrates a user’s visual perception and his/her ergonomic perception. Specifically, the approach enables users to manipulate an information space to follow the behavior of human movement in the 3D world with six degrees of freedom. Users can move an information space along the x-, y-, and z-axes as well as rotate it-giving six degrees of freedom. Manipulating the information space in this way provides users with a more comprehensive understanding of the space, and integrates their visual capability and ergonomic perception so that they can fully utilize their mental capabilities.     

Accessible Ubiquitous Services for Supporting Daily Activities: A Case Study with Young Adults with Intellectual Disabilities

ABSTRACT

Ubiquitous environments have considerable potential to provide services supporting daily activities (using public transportation to and from workplace, using ATM machines, selecting and purchasing goods in ticketing or vending machines, etc.) in order to assist people with disabilities. Nevertheless, the ubiquitous service providers generally supply generic user interfaces which are not usually accessible for all potential end users. In this article, a case study to verify the adequacy of the user interfaces automatically generated by the Egoki system for two supporting ubiquitous services adapted to young adults with moderate intellectual disabilities was presented. The task completion times and the level of assistance required by participants when using the interfaces were analyzed. Participants were able to access services through a tablet and successfully complete the tasks, regardless of their level of expertise and familiarity with the service. Moreover, results indicate that their performance and confidence improved with practice, as they required fewer direct verbal and pointer cues to accomplish tasks. By applying observational methods during the experimental sessions, several potential improvements for the automated interface generation process were also detected.     

The influence of success experience on self-efficacy when providing feedback through technology

Background: as a high level of self-efficacy is associated with bigger behavioral changes as well as to higher levels of physical activity, the development and implementation of strategies that successfully improve self-efficacy are important to technological interventions. We performed an experiment to investigate whether self-efficacy regarding a specific task can be influenced by using feedback strategies that focus on success experience and are provided through technology. Method: subjects were asked to walk from A to B in exactly 14, 16 or 18 s, wearing scuba fins and a blindfold. The task guaranteed an equal level of task experience among all subjects at the start of the experiment and makes it difficult for subjects to estimate their performance accurately. This allowed us to manipulate feedback and success experience through technology-supported feedback. Results: subjects’ self-efficacy regarding the task decreases when experiencing little success and that self-efficacy regarding the task increases when experiencing success. This effect did not transfer to level of self-efficacy regarding physical activity in general. Graphical inspection of the data shows a trend towards a positive effect of success experience on task performance. Conclusion: experiencing success is a promising strategy to use in technology-supported interventions that aim at changing behavior, like mobile physical activity applications.