CHAIR Metrics: Quantifying Seated Behavior

• CHAIR metrics are quantitative features derived from sensor-equipped chairs that objectively capture human seated behavior.
• They are computed from high-frequency accelerometer and gyroscope data using fixed thresholds and statistical methods over defined epochs.
• These metrics empower machine learning models for assessing esports skills, clinical risk, and rehabilitation performance.

CHAIR metrics are a family of quantitative features derived exclusively from instrumented chairs equipped with motion or pressure sensors, designed for objective analysis of human chair-seated behavior in contexts ranging from esports and rehabilitation to clinical risk monitoring. These metrics form the basis for statistical inference and machine learning applications targeting skill identification, activity profiling, physical assessment, or safety analysis. The most fully articulated and evaluated CHAIR metrics to date appear in sensorimotor behavior studies of esports athletes using smart chair platforms with high-frequency accelerometer and gyroscope logging (Smerdov et al., 2019, Smerdov et al., 2019).

1. Definition and Computation of CHAIR Metrics

The CHAIR metric suite comprises scalar features, each formally defined as a deterministic function of high-resolution time series captured from chair-mounted sensors. The foundational example in Smerdov et al. is built from a fixed configuration:

• Sensor data: $a_x(t), a_y(t), a_z(t)$ (three-axis acceleration), $g_x(t), g_y(t), g_z(t)$ (three-axis angular velocity) sampled at 100 Hz.
• Metrics computed over a defined epoch (e.g., a full 35-minute session, or partitioned into overlapping/non-overlapping 3-minute windows).

Metrics fall into three main categories:

1. Active Movement Features (portion of session exceeding high-magnitude motion relative to baseline):

$$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$

(and analogously for $a_y$, $a_z$, $g_x$, $g_y$, $g_z$).

2. Subtle Oscillation Features (variance within “quiet” periods):

$$axo = \frac{1}{N_{xo}} \sum_{t:\, |a_x - \mu_{a_x}| \le 3\sigma_{a_x}} (a_x(t) - \mu_{a_x})^2$$

(and analogously for all axes of accelerometer and gyroscope).

3. Discrete Posture/Lean-Back Ratio:

$$lb = \frac{1}{T} \sum_{t=1}^T \mathbf{1}(a_z(t) < \tau)$$

where $g_x(t), g_y(t), g_z(t)$0 is calibrated so that $g_x(t), g_y(t), g_z(t)$1 indicates the user pressing into the chair back.

All such metrics are purely statistical with respect to the physical readings; no in-game or external annotations are used in their construction (Smerdov et al., 2019).

2. Feature Extraction and Signal Processing

The CHAIR metrics extraction pipeline is characterized by minimal preprocessing and simple, robust statistical decision rules:

• Raw samples are taken at high rate (typically 100 Hz) and divided into analysis windows of fixed duration (e.g., 3 minutes).
• For each axis, sample mean and standard deviation are computed.
• Movement state is assigned per-sample by absolute deviation; a threshold of 3 standard deviations from the window mean.
• “Active” and “quiet” samples are defined by this thresholding, with subsequent fraction-of-time and variance calculations indexed accordingly.
• The lean-back metric uses a fixed, empirically determined threshold rather than dynamic normalization.
• No explicit temporal filtering or multidimensional combination is performed prior to metric calculation.
• All features are used directly in vectorized form as machine learning inputs, with no further dimensionality reduction or engineered composites (Smerdov et al., 2019).

This approach is replicated in similar work analyzing in-game event-aligned posture dynamics, where median rolling-window standard deviations (“floating std”) of raw signals are used for movement detection. Where event-aligned features are calculated, these are simply temporally restricted averages or movement counts in fixed windows post-event (e.g., after deaths or shootout in esports) (Smerdov et al., 2019).

3. Mathematical Formulation and Categories: Summary Table

Metric Group	Channels/Quantity	Mathematical Expression (LaTeX)
Active Movement	$g_x(t), g_y(t), g_z(t)$2, $g_x(t), g_y(t), g_z(t)$3, $g_x(t), g_y(t), g_z(t)$4, $g_x(t), g_y(t), g_z(t)$5, $g_x(t), g_y(t), g_z(t)$6, $g_x(t), g_y(t), g_z(t)$7	$g_x(t), g_y(t), g_z(t)$8
Subtle Oscillation	$g_x(t), g_y(t), g_z(t)$9, $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$0, $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$1, $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$2, $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$3, $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$4	$$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$5
Lean-Back	$$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$6	$$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$7

Subsequent studies, such as those instrumenting chairs with independent strain-gauge load cells, have not yet formalized additional derived CHAIR metrics beyond raw pressure time series per sensor array, deferring analytical feature definitions to future work (Yeh et al., 2022).

4. Integration into Machine Learning Frameworks

CHAIR metrics are principally intended as direct input features for supervised machine learning classifiers targeting behavioral or health-related outcomes. In the most evaluated implementations:

• Feature matrix $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$8 (for 13 CHAIR metrics) is constructed, where $$axn = \frac{1}{T} \sum_{t=1}^T \mathbf{1}\left(|a_x(t) - \mu_{a_x}| > 3 \sigma_{a_x}\right)$$9 is the number of sessions/windows.
• Binary or categorical targets (e.g., high vs. low skill, clinical event occurrence) are encoded as $a_y$0.
• Algorithms evaluated include logistic regression, soft-margin support vector machines, $a_y$1-nearest neighbors, and random forests.
• Performance is quantified via ROC AUC, accuracy, and log-loss, typically under leave-player(s)-out cross-validation or repeated random splits (Smerdov et al., 2019, Smerdov et al., 2019).

Empirical findings show that the strongest predictors of high skill in esports settings are subtle oscillation metrics (indicative of nuanced micromovements), while high values of active movement or frequent lean-back signature are linked to lower skill (Smerdov et al., 2019).

5. Statistical and Behavioral Interpretation

CHAIR metrics provide interpretable behavioral correlates:

• Active movement features are generally negatively correlated with high-skill or expert status (Pearson $a_y$2 to $a_y$3), implying that excessive or frequent chair repositioning is a marker of inexperience or lower cognitive-motor control.
• Subtle oscillation features (e.g., gyo, axo) are positively correlated ($a_y$4 up to $a_y$5), suggesting that controlled, low-amplitude oscillatory postural adjustments are hallmarks of expertise.
• Lean-back ratio correlates negatively with skill ($a_y$6), consistent with the interpretation that reclined posture signals disengagement or reduced focus (Smerdov et al., 2019).
• These patterns are robust across classifier families and train/test splits. Logistic regression and random forest feature importances consistently highlight subtle oscillatory metrics as the predominant predictive signals.

A parallel (but as yet undeveloped) extension appears in clinical settings: by instrumenting chairs for pressure mapping, the potential exists to quantify postural control, movement smoothness, or functional transitions (e.g., sit-to-stand), but no scalar CHAIR metrics beyond raw signals have been published to date (Yeh et al., 2022).

6. Alternative CHAIR Metrics in Clinical Monitoring

Distinct from sensorimotor or esports contexts, CHAIR metrics in the hospital environment can refer to exposure-normalized outcomes (e.g., chair-associated fall rates). Here, the metric is explicitly defined via:

• Exposure hours: Fractional chair occupancy time $a_y$7 hours.
• Probability-weighted fall rate: $a_y$8 falls per 1,000 chair-hours.
• Adjusted rate ratio (via Poisson GLM): $a_y$9, comparing chair vs. bed, offset by log-exposure, and adjusted for multiple covariates.

This framework allows precise quantification of denominator-specific risk and supports causal modeling and intervention targeting (e.g., safe chair setup protocols) (Gabriel et al., 24 Mar 2026).

7. Limitations and Future Trajectories

• All currently published CHAIR metrics for behavior/skill inference are engineered solely from accelerometer/gyroscope time series and rely on straightforward thresholds and variances. No spectral or nonlinear dynamical metrics have been openly defined or assessed.
• Broader clinical applications (e.g., rehabilitation, mobility assessment) report only raw sensor traces; systematic feature engineering in such contexts remains unpublished (Yeh et al., 2022).
• Exposure-based CHAIR metrics in clinical risk monitoring are tightly linked to AI-based location inference and adjudication protocols; their transferability depends on the accuracy and generalizability of the underlying detection platform (Gabriel et al., 24 Mar 2026).
• A plausible implication is that future research, leveraging richer sensor data and annotated clinical or behavioral targets, may extend the CHAIR metrics taxonomy with domain-specific indices for fallback risk, muscle function, or ergonomic risk.

In summary, CHAIR metrics constitute a rigorously defined toolbox for quantifying physical behavior on instrumented chairs, with demonstrated relevance for skill assessment, behavior profiling, and risk quantification across technical, athletic, and medical domains (Smerdov et al., 2019, Smerdov et al., 2019, Gabriel et al., 24 Mar 2026, Yeh et al., 2022).