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This case was judged by an anonymized AI reviewer and has not been independently reviewed by people; inspect the draft, cards, and both outputs to assess it.
AutocompleteWith cards better79 / 280 · beu-52ca4168f38187d0

Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

Psychology and cognitive neuroscience · 2202.03033v2

FLOWING EVIDENCE BENCHMARK

How do we compare writing with and without cards?

At the same writing position, the same model produces one first result with cards and one without. All 280 pairs enter the overall anonymized review, which assesses whether a decision not to continue fits the draft.

Overall: 138 with-card wins, 26 ties, 107 no-card wins, 5 pairs where neither was usable, and 4 inconclusive judgments. Both sides wrote text in 218 pairs; 48 had a deliberate stop and 14 included an invalid output.

SAME DRAFT · WITH OR WITHOUT CARDS

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED VERSIONS

SHARED BY BOTH

Draft, writing task, and generation model

A · With cards

Relevant literature cards supplied

B · Without cards

No literature cards supplied

Generation model

Same model and version

A → first output

B → first output

Anonymized AI judge

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Review again with the answer order swapped

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The AI judge sees the same draft and both first outputs without knowing which received cards.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are positions and cards selected?

We selected 40 writing positions in each of seven fields, for 280 comparisons. The position and relevant literature cards were set before each comparison, so both versions began from the same draft.

The main result retains every frozen position, including places where cards might not add a useful next move. This avoids selecting cases based on their outputs, but 49% describes the whole cohort, not a win rate where evidence clearly had an opportunity to help. This cohort did not record that opportunity label for each position before generation, so those two groups cannot yet be separated reliably.

CARD USE · 140/280

Reported card use in continuations with text

The model reports whether a card affected its continuation. In 140 of 280 tests it produced continuation text and reported card use. The previous count of 150 included 10 cases without continuation text and has been corrected.

COMPARISON · 70 / 7 / 30

Which written version was better?

Of the 107 reported-use cases where both versions produced text, the blinded AI judge preferred the with-card version 70 times, found 7 about the same, and preferred the without-card version 30 times.

How are the two table percentages calculated?

ADOPTION COVERAGE

140reported use with text
÷
280all positions
=
50%

WITH-CARD VERSION PREFERRED

70with cards better
÷
107comparable tests
=
65%

The percentages use different groups: 50% counts continuations with text reporting card use among all 280 tests; 65% covers the 107 such cases with text on both sides. The generating model reports card use, while an anonymized AI judge compares writing quality.

Card use is self-reported by the generator. The anonymized AI judge sees the draft and source snippets, so source-aware preference remains possible; there was no independent human review. The overall result describes these 280 frozen positions, not a win probability for any writing task.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

6 Future Directions

… eled as ”non-stressed” in subjective labeling ( Liapis et al., 2015 ) . This type of labeling ambiguity degrades the performance of the system and thus needs to be rectified by developing a more robust mechanism for labeling the data.

…all the devices whether they are EEG headsets, skin conductance, and heart rate measurement modules or smartwatches all have limited battery life and can last approximately around 4-5 hours. But to record the activities of a user for a complete day, the power consumption of the devices needs to be kept at a minimum level so that the battery can work for more time. Existing literature has not examined this power consumption factor and needs to be addressed to be able to manufacture efficient and long-lasting data acquisition devices for monitoring stress, especially for real-life environments.

Noise affects the data acquisition part of the stress recognition measurement in the lab as well as the out-of-lab environment. Physiological signals which include EEG, EMG, ECG, PPG, and RR are most commonly affected by the individual body movement parts and severely degrade the signal quality. Artifact removal techniques include low, high, and bandpass filtering, least mean square, notch filtering, recursive least mean square, principal component analysis (PCA), independent component analysis (ICA), and wavelet denoising. Even though these techniques have been found quite useful in the removal of noise from the bio-signals in the lab environment, but still the data acquired out-of-lab presents a wider range of challenges like environmental conditions

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionOverall AI review: With cards better

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Read overall AI review rationale

A = with cards · B = without cards

Both outputs correctly identify that the sentence needs to continue the contrast between lab and out-of-lab challenges, specifically naming 'motion artifacts' as a key out-of-lab challenge alongside environmental conditions. Both are grammatically insertable (fixing the 'but still' issue by providing the needed continuation). Output A is slightly superior because 'fixed filtering pipelines' is more precise and technically appropriate than 'same techniques'—it echoes the specific artifact removal techniques just listed (filtering, PCA, ICA, wavelet denoising) and emphasizes that out-of-lab data requires adaptive rather than fixed approaches. 'Filtering pipelines' also better captures the methodological sequence implied by the listed techniques. Output B's 'same techniques' is slightly more generic and less connected to the preceding enumeration of methods.

Evidence

Same model and position · retrieved passages supplied · first output

Preferred by blind review

and motion artifacts that are difficult to characterize and remove with the same fixed filtering pipelines.

Passages supplied to the Evidence version

Card 1 · Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness track…

Read full passage excerpt

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness tracker can go for up to 7 days on a single charge. Continuous activity and heart rate monitoring speed up the depletion of the battery of most wearables. Gathering and processing of simultaneous sensor data can further increase the power consumption of the devices.

Card 2 · Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable…

Read full passage excerpt

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).

Card 3 · Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future.…

Read full passage excerpt

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to n …

Card 4 · Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes…

Read full passage excerpt

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for every …

Card 5 · Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness track…

Read full passage excerpt

The battery life of smartwatches and fitness trackers vary greatly depending on the features and functionality available on-board the wearable. The Apple Watch Series 5, which is more of a lifestyle and fitness tracker, can run for a period of up to 18 hours whereas the Fitbit Charge 3 fitness tracker can go for up to 7 days on a single charge. Continuous activity and heart rate monitoring speed up the depletion of the battery of most wearables. Gathering and processing of simultaneous sensor data can further increase the power consumption of the devices.

Model-reported card use

The model did not report that any card materially affected this completion.

No Evidence

Same model and position · no retrieved passages · first output

Not preferred in this comparison

and motion artifacts that are difficult to characterize and remove with the same techniques.

Earlier card-use comparison

The earlier diagnostic compared only cases with reported card use and text on both sides; its scope differs from the overall AI review above.

This case does not have both with-card continuation text and a model-reported use record. It remains among all 280 tests but does not enter the reported-use comparison.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.