Smartphone Sensing Technologies for Tailored Parkinson’s Disease Diagnosis and Monitoring

Postolache, Gabriela; Postolache, Octavian

doi:10.1007/978-3-319-93491-4_13

Cited by 5 publications

(4 citation statements)

References 67 publications

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“…Smartphones are owned by 81% of adults in the United States [ 18 ], contain one or several embedded inertial sensors and have been validated as a platform for ecological movement analysis [ 16 , 19 – 22 ]. Benefits in terms of healthcare accessibility, patient engagement and reduction of clinician workloads are also envisioned or already proven to emerge from the use of mobile health technologies [ 23 ]. However, many studies addressing movement analysis using smartphone sensors have requirements that do not reflect typical usage, reducing ecological validity, and validation of applications for gait analysis of PD patients is limited [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

Alberto¹,

Cabral

Proença³

et al. 2021

BMC Neurol

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Background Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

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Section: Introductionmentioning

confidence: 99%

Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

Alberto¹,

Cabral

Proença³

et al. 2021

BMC Neurol

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“…Most previous reviews have focused on specific aspects of smartphone sensing. For example, prior works reviewed the literature with a focus on specific conditions or set of conditions (e.g., Parkinson’s [ 31 ], drug use [ 38 ], mental health [ 127 , 128 ], and physical activity [ 129 ]), whilst others narrowed their purview by some studying specific forms of data collection (e.g., EMAs [ 130 ], multimedia sensors [ 116 ]), and analysis approaches [ 40 ]. In contrast, our review presents a broad overview of all such factors and does not focus on specific conditions and sensing approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Secondly, there are numerous systematic reviews that provide a comprehensive insight on smartphone sensing for various specific health conditions [ 31 , 36 , 37 , 38 ], smartphone-based activity recognition [ 39 , 40 ], and sensing apps and datasets [ 41 ]. Rather than replicating these and providing a quantitative insight into the fields, a narrative review allows for a qualitative approach to highlight overall advancements.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have utilized smartphones to remotely collect data from users and gain health insights from these data. Studies have explored how smartphone-based sensing can be used to infer mood [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], physical activity [ 3 , 11 , 22 , 23 ], and mental health [ 13 , 24 , 25 , 26 , 27 ], and also to track the effects of chronic health conditions [ 28 , 29 , 30 , 31 ]. Moreover, software frameworks have also been developed to help researchers use smartphone sensing to conduct their own studies, such as AWARE [ 32 ], Beiwe [ 33 ], and EARS [ 34 ] to name a few.…”

Section: Introductionmentioning

confidence: 99%

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Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

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Understanding practices and needs of researchers in human state modeling by passive mobile sensing

Mankoff

Dey

2021

CCF Trans. Pervasive Comp. Interact.

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Passive mobile sensing for the purpose of human state modeling is a fast-growing area. It has been applied to solve a wide range of behavior-related problems, including physical and mental health monitoring, affective computing, activity recognition, routine modeling, etc. However, in spite of the emerging literature that has investigated a wide range of application scenarios, there is little work focusing on the lessons learned by researchers, and on guidance for researchers to this approach. How do researchers conduct these types of research studies? Is there any established common practice when applying mobile sensing across different application areas? What are the pain points and needs that they frequently encounter? Answering these questions is an important step in the maturing of this growing sub-field of ubiquitous computing, and can benefit a wide range of audiences. It can serve to educate researchers who have growing interests in this area but have little to no previous experience. Intermediate researchers may also find the results interesting and helpful for reference to improve their skills. Moreover, it can further shed light on the design guidelines for a future toolkit that could facilitate research processes being used. In this paper, we fill this gap and answer these questions by conducting semi-structured interviews with ten experienced researchers from four countries to understand their practices and pain points when conducting their research. Our results reveal a common pipeline that researchers have adopted, and identify major challenges that do not appear in published work but that researchers often encounter. Based on the results of our interviews, we discuss practical suggestions for novice researchers and high-level design principles for a toolkit that can accelerate passive mobile sensing research.

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Smartphone Sensing Technologies for Tailored Parkinson’s Disease Diagnosis and Monitoring

Cited by 5 publications

References 67 publications

Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Understanding practices and needs of researchers in human state modeling by passive mobile sensing

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