Aharon Satt

Harnessing the crowds for smart city sensing

In this work we discuss the challenge of harnessing the crowd for smart city sensing. Within a citys context, such reports by citizen or city visitor eye witnesses may provide important information to city officials, additionally to more traditional data gathered by other means (e.g., through the citys control center, emergency services, sensors spread across the city, etc). We present an high-level overview of a novel crowd sensing system that we develop in IBM for the smart cities domain. As a proof of concept, we present some preliminary results using public safety as our example usecase.

Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

To evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early‐stage Alzheimers disease (AD).

Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment

The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and nonemergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system. This study proposes public safety data quality criteria which were developed to assess and identify the problems affecting data quality. This study is guided by design science methodology and applies three driving theories: the data information knowledge action result (DIKAR) model, the characteristics of a smart city, and a credible data quality framework. Four critical success factors were developed to ensure that high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.

INNOVATIVE VOICE ANALYTICS FOR THE ASSESSMENT AND MONITORING OF COGNITIVE DECLINE IN PEOPLE WITH DEMENTIA AND MILD COGNITIVE IMPAIRMENT

ualized interactional care guide based on data collected from resident assessments, observation, and interviews with residents’ family members and home staff. RM-ANOVAwas used to analyze the outcome data. Results: Indices of feasibility showed that the recruitment and adherence rate were 86.6%% and 94%, respectively. In the 2 month control phase, there were significant declines in the TUG (4.15 seconds, P1⁄40.01), 2MWT (-5.77 meters, P1⁄40.03), FIM-motor (-12.52, P1⁄40.00), FIM-cognition (-5.36, P1⁄40.00), and QOL (-1.84, P1⁄40.03). After the MWI, there was a significant improvement in all outcomes: TUG (-8.85 seconds, P1⁄40.00), 2MWT (27.47 meters, P1⁄40.00), FIM-motor (0.72, P1⁄40.00), FIM-cognition (5.88, P1⁄40.00), and QOL (2.44, P1⁄40.05). The intervention was able to negate the decline experienced during the control period. The acceptance of the intervention from family members and staff was measured with a survey and rated as “very highly acceptable”. Conclusions: Participants improved their functional mobility, ADL function, and QOL after the 4-month MWI compared to usual care. This study showed that it is feasible to conduct an individualized walking intervention with NH residents with dementia, and provides robust evidence that can inform a future large-scale RCT to further evaluate the intervention.

THE DEM@CARE PROJECT SPEECH RECORDING AND AUTOMATIC ANALYSIS FOR THE ASSESSMENT OF ALZHEIMER DISEASE AND RELATED DISORDERS

increase the risk for developing AD. Results: The study population consisted of 183 MCI patients at baseline. At follow-up, 74 patients were stable and 109 patients progressed to AD. The presence of significant depressive symptoms in MCI as measured by the CSDD (HR: 2.06; 95% CI: 1.23 3.44; p1⁄40.011) and the GDS-30 (HR: 1.77; 95% CI: 1.10 2.85; p1⁄40.025) were associated with an increased the risk of progression to AD. The severity of depressive symptoms as measured by the GDS-30 was a predictor for progression too (HR: 1.06; 95% CI: 1.01 1.11; p1⁄40.020). Furthermore, also the severity of agitated behavior, especially verbal agitation, and the presence of purposeless activity were associated risk factors for progression, whereas diurnal rhythm disturbances in our study was associated with a decreased risk of progression. Conclusions: Depressive symptoms in MCI appear to be associated with an increased risk of progression to AD.