e.g. mhealth
Search Results (1 to 6 of 6 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 5 JMIRx Med
- 1 Journal of Medical Internet Research
- 0 Medicine 2.0
- 0 Interactive Journal of Medical Research
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Medical Informatics
- 0 JMIR Public Health and Surveillance
- 0 JMIR mHealth and uHealth
- 0 JMIR Serious Games
- 0 JMIR Mental Health
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 JMIR Formative Research
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Aging
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
The self-reported benefit is absolutely important, but based on the Introduction, the reader is interested in knowing how objective speech recognition improved with the wristband for the selected consonants. If these data are available, it would be helpful to add them. If not, it would be helpful if the authors could explain—somewhere in the manuscript—why this testing was not completed/reported.
JMIRx Med 2024;5:e55728
Download Citation: END BibTex RIS
Go back to the top of the page Skip and go to footer section
Go back to the top of the page Skip and go to footer section
Response: We have added the following segment to the Results section:
“Time wearing the wristband and time exposed to speech was verified through collection of data from backend logging that records when the wristband is turned on or off and when a phoneme is detected. As seen in Figure 5 participants wore the wristband for and average of 12.9 (SD=8.1) hours per day and were exposed to speech for an average of 6.7 (SD=3.3) hours per day.”
“One potential hypothesis” should be “One potential explanation.”
JMIRx Med 2024;5:e55510
Download Citation: END BibTex RIS
The wristband receives sound from the environment through an onboard microphone and uses a machine learning algorithm to filter background noise (BN) and extract target phonemes from speech. Each phoneme signal is mapped to its own unique linear resonant actuator (LRA) in the strap of the wristband where it is felt as a vibration on the skin. There are four LRAs embedded within the wristband strap, giving each target phoneme a unique spatial location on the wrist.
JMIRx Med 2024;5:e49969
Download Citation: END BibTex RIS
However, accuracy needs to be assessed for all new smartwatch or wristband–standalone PPG-AF algorithm combinations, and this is lacking in literature.
The primary aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after CV. The secondary objective was to find patient-related predictors of a bad-quality PPG signal.
J Med Internet Res 2023;25:e44642
Download Citation: END BibTex RIS