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Wi-Fi body scale, model HS5 is indicated to measure body weight, estimate body fat, body water percentage, body muscle mass, bones mass, visceral fat rating and daily calorie intake (DCI) using BIA (bioelectrical impedance analysis). This product is for use by generally healthy adults,,who are not ill, feverish, have a chronic or acute disease, or a condition that affect the level of hydration such as pregnancy.

The patient steps on the scale device, where four electrodes are located. The patient must step on the electrodes with bare feet, with normal moisture. Through harmless current stimulation of 500 uA, at 50 kHz, the Wi-Fi body scale calculates the body fat percentage. This calculation is done via the Bioelectrical Impedance Method. The current is passed through the body and the impedance of the body determines the body fat. The calculation is based upon electrical impedance, height, weight, age, and gender. The calculation is performed via internal software, which uses the variables programmed in by the user. Wi-Fi body scale, model HS5 can be used with an iPod Touch, iPhone or iPad.

The provided text describes the "Wi-Fi body scale, model HS5" and its predicate device, the "Body Analysis Scale, Model BG 17 (K110928)". This is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting detailed clinical trial results with specific acceptance criteria and performance metrics for a novel device.

Therefore, many of the requested details regarding acceptance criteria, specific study design elements (sample size for test set, data provenance, expert qualifications, adjudication, MRMC study, standalone performance, training set details), and ground truth establishment are not explicitly provided in this document.

However, I can extract the following information based on the text:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table of acceptance criteria with specific quantitative targets for "Wi-Fi body scale, model HS5." It states that "Clinical testing was used to validate the effectiveness and accuracy of the device. All test results were satisfactory." This is a qualitative statement of compliance rather than a presentation of specific performance metrics against predefined acceptance criteria.

The "Comparison to the predicate device" section mentions that the Wi-Fi body scale, model HS5 is "substantially equivalent" to the predicate device. This implies that its performance is considered acceptable if it matches or is sufficiently similar to the predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for test set: Not explicitly stated. The document refers to "Clinical testing" but does not give the number of participants.
• Data provenance: Not explicitly stated. The document does not mention the country of origin of the data or whether the study was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable or not provided. This device measures physiological parameters directly. While its accuracy might be compared to a "gold standard" measurement method, there's no indication of "experts" establishing a ground truth in the context of interpretation, as might be the case for imaging devices.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable or not provided. Adjudication methods are typically used when multiple human readers interpret data that requires expert consensus for ground truth, which is not the type of data or evaluation described for this device.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is a standalone measurement tool (a body scale) and does not involve human readers interpreting data or an AI assisting in interpretation. It directly calculates body composition parameters using Bioelectrical Impedance Analysis (BIA).

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, implicitly. The device itself performs the calculations using the Bioelectrical Impedance Method. The performance described ("Clinical testing was used to validate the effectiveness and accuracy of the device. All test results were satisfactory.") refers to the device's ability to produce these measurements independently. There is no human interaction in the calculation process or "in-the-loop" assistance for the device's function.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not explicitly stated. For body composition devices like this, the "ground truth" for clinical testing would typically involve comparison to established, more accurate body composition measurement methods (e.g., DEXA scan, hydrostatic weighing, or other validated impedance devices) rather than expert consensus or pathology. The document only mentions "Clinical testing was used to validate the effectiveness and accuracy of the device," without specifying the precise ground truth method.

8. The sample size for the training set

Not applicable or not provided. The document describes a medical device, not a machine learning model that would typically have a "training set" for an algorithm in the traditional sense, though the internal software performs calculations based on an algorithm. The 510(k) summary focuses on the device as a whole and its "clinical testing" or validation, not the development of a specific algorithm within it.

9. How the ground truth for the training set was established

Not applicable or not provided, as there is no mention of a "training set" for a machine learning algorithm in the context of this 510(k) summary. The BIA method is a well-established physiological principle, and the device implements this method through internal software.

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