Recognition of Human Activities via Wearable Sensors

Nardin, Luciana de; Rodrigues, Kamila Rios da Hora; Zimmermann, Larissa Cardoso; Pimentel, Maria da Graça Campos

doi:10.1145/3428658.3431086

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Mobile applications have collaborated to recognize relevant users' information through monitoring data, such as usage logs, and users' context and daily routines (De Nardin et al, 2020). In this perspective, several studies have proposed using mobile apps to prevent, diagnose, and treat diseases (Rodríguez and Wägner, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring User Experience and Usability of mHealth applications for people with diabetes: An Evaluation Study Using UEQ and HE4EH Checklist

Rodrigues,

Moura,

Branco

et al. 2023

JIS

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Diabetes affects 10% of adults aged 20 to 79 globally, caused by insufficient insulin production. Mobile apps are effective in managing this chronic condition. This study aimed to investigate the experience of using mobile applications for healthcare patients with diabetes in their daily activities. Ten participants diagnosed with diabetes were invited to evaluate five Android mobile applications using a predefined protocol. We used the User Experience Questionnaire (UEQ) to assess the usability and user experience of the apps. In addition, the Heuristic Evaluations for mHealth (HE4EH) checklist was used by three domain experts to assess the apps. The results showed that one of the five apps had issues with the stimulus and novelty aspects of user experience. Among all the apps, the novelty had the lowest Likert scale value. While most apps had creative designs and attractive interfaces, the heuristic assessment revealed many violations and concerns about instruction and suitability due to a lack of crucial information for monitoring user routines.

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Section: Introductionmentioning

confidence: 99%