Media Review: The Owlet Smart Sock—a “must have” for the baby registry?

Malik, Afeerah; Ehsan, Zarmina

doi:10.5664/jcsm.8400

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…However, consumers who experience negative psychological outcomes due to poor performance will provide more negative product recommendations. A recent research on baby monitoring has stated lack of measurement accuracy of OSS (Malik and Ehsan, 2020). Therefore, Owlet Baby Care Inc. should focus on the accurate measurement of their health vitals as well as SIDS prevention to have a positive impact on society.…”

Section: Discussionmentioning

confidence: 99%

Exploring online consumer reviews of wearable technology: The Owlet Smart Sock

Hasan

Stannard

2022

RJTA

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Purpose The purpose of this research is to analyze consumers’ post-purchase reviews of the Owlet Smart Sock (OSS) and investigate the factors influencing continued use and product recommendation. Design/methodology/approach The unified theory of acceptance and use of technology 2 and the privacy calculus model were used to focus both on technology and privacy aspects of OSS – a wearable technology product for baby monitoring. A sample of 450 online consumer review data was collected from Owletcare.com and Amazon.com. The data analysis was done by using NVivo 11. Findings Findings show that effort expectancy, price value and performance expectancy played the most striking role during adoption, continued future use and recommendation to others, whereas perceived privacy risk had the least importance. Research limitations/implications Consumer empowerment through online reviews plays a crucial role in conveying their specific needs and desires to both manufacturers and other prospective consumers. The research is also expected to contribute research and development of technology-integrated products. Practical implications The research findings will provide valuable insights for manufacturers and retailers to understand consumers’ actual preferences and acceptance during the use of wearable technology. Originality/value This study extends research work on consumer use behavior by evaluating online reviews that provide them the opportunity to express their satisfaction and concerns. Insights from experience consumers’ reviews facilitate designers, developers and manufacturers to have a strategic focus during wearable technology development.

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Section: Discussionmentioning

confidence: 99%

Exploring online consumer reviews of wearable technology: The Owlet Smart Sock

Hasan

Stannard

2022

RJTA

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“…Although systems such as the Owlet Smart Sock have had substantial success on a commercial scale, 50 independent analysis raises questions about the system's accuracy at detecting neonatal hypoxaemia and bradycardia compared with FDA-cleared systems. [51][52][53] Furthermore, home monitoring of cardiorespiratory function in neonates has not success fully prevented sudden unexpected deaths in infants. 54 Other examples include MonBaby, a small Bluetooth enabled rigid device, that tracks body motion and respiratory motion.…”

Section: Alternative Technology Development Efforts In Neonatal Monitoringmentioning

confidence: 99%

Wireless skin sensors for physiological monitoring of infants in low-income and middle-income countries

Rwei

Vwalika

et al. 2021

The Lancet Digital Health

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Globally, neonatal mortality remains unacceptability high. Physiological monitoring is foundational to the care of these vulnerable patients to assess neonatal cardiopulmonary status, guide medical intervention, and determine readiness for safe discharge. However, most existing physiological monitoring systems require multiple electrodes and sensors, which are linked to wires tethered to wall-mounted display units, to adhere to the skin. For neonates, these systems can cause skin injury, prevent kangaroo mother care, and complicate basic clinical care. Novel, wireless, and biointegrated sensors provide opportunities to enhance monitoring capabilities, reduce iatrogenic injuries, and promote family-centric care. Early validation data have shown performance equivalent to (and sometimes exceeding) standard-of-care monitoring systems in premature neonates cared for in high-income countries. The reusable nature of these sensors and compatibility with low-cost mobile phones have the future potential to enable substantially lower monitoring costs compared with existing systems. Deployment at scale, in low-income countries, holds the promise of substantial improvements in neonatal outcomes.

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“…In fact, it is inconvenient for babies to always wear these pads or sensors correctly when they sleep. Second, these baby monitors often send out false alarms, which can increase the anxiety of many parents [13,14]. Therefore, parents are most likely to suffer from increased stress or even depression, which affects their sleep quality and emotion.…”

Section: Introductionmentioning

confidence: 99%

Memory-Efficient AI Algorithm for Infant Sleeping Death Syndrome Detection in Smart Buildings

Huang

Hsieh²,

Hsieh³

et al. 2021

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Artificial intelligence (AI) is fundamentally transforming smart buildings by increasing energy efficiency and operational productivity, improving life experience, and providing better healthcare services. Sudden Infant Death Syndrome (SIDS) is an unexpected and unexplained death of infants under one year old. Previous research reports that sleeping on the back can significantly reduce the risk of SIDS. Existing sensor-based wearable or touchable monitors have serious drawbacks such as inconvenience and false alarm, so they are not attractive in monitoring infant sleeping postures. Several recent studies use a camera, portable electronics, and AI algorithm to monitor the sleep postures of infants. However, there are two major bottlenecks that prevent AI from detecting potential baby sleeping hazards in smart buildings. In order to overcome these bottlenecks, in this work, we create a complete dataset containing 10,240 day and night vision samples, and use post-training weight quantization to solve the huge memory demand problem. Experimental results verify the effectiveness and benefits of our proposed idea. Compared with the state-of-the-art AI algorithms in the literature, the proposed method reduces memory footprint by at least 89%, while achieving a similar high detection accuracy of about 90%. Our proposed AI algorithm only requires 6.4 MB of memory space, while other existing AI algorithms for sleep posture detection require 58.2 MB to 275 MB of memory space. This comparison shows that the memory is reduced by at least 9 times without sacrificing the detection accuracy. Therefore, our proposed memory-efficient AI algorithm has great potential to be deployed and to run on edge devices, such as micro-controllers and Raspberry Pi, which have low memory footprint, limited power budget, and constrained computing resources.

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Media Review: The Owlet Smart Sock—a “must have” for the baby registry?

Cited by 4 publications

References 8 publications

Exploring online consumer reviews of wearable technology: The Owlet Smart Sock

Exploring online consumer reviews of wearable technology: The Owlet Smart Sock

Wireless skin sensors for physiological monitoring of infants in low-income and middle-income countries

Memory-Efficient AI Algorithm for Infant Sleeping Death Syndrome Detection in Smart Buildings

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