---
title: 'SonarWatch: Ultrasonic Sensing for Smartwatches'
url: https://www.emergentmind.com/topics/sonarwatch
type: topic
---

# SonarWatch: Ultrasonic Sensing for Smartwatches

SonarWatch denotes an ultrasonic field-sensing and gesture-recognition technique for wrist-worn smartwatches, leveraging standard audio transducers and inertial measurement units (IMUs) to enable rich, low-power, continuous interaction without additional hardware. The system synthesizes an active acoustic field (16.5–20 kHz) using the built-in speaker, receives the reflected field via two microphones, and fuses these ultrasonic sensing channels with kinematic data from a 9-axis IMU via a multi-modal machine learning pipeline, yielding robust recognition rates for explicit and fine-motor on-wrist gestures in diverse acoustic environments [2408.12689].

## 1. Hardware Architecture and Sensing Modality

SonarWatch repurposes commodity smartwatch hardware—specifically, a standard left-mounted speaker (driven at 48 kHz), two microphones (one bezel-mounted, one under-screen), and a central Hi221 9-axis IMU (sampled at 200 Hz). The system emits a continuous cycle of ultrasonic chirps (16.5 kHz up to 20 kHz and back), partitioned into blocks of 4,096 samples (≈85 ms per “chunk”), at a rate of ~11.7 Hz [2408.12689]. Ultrasonic reflections from the wrist, hand, or proximate objects are transduced by the microphones, digitized (using, e.g., a TASCAM DR-05X as a 48 kHz sound card), and fed, along with IMU data over Bluetooth, to the computing unit (typically a Raspberry Pi 4B or MacBook Pro for experimental setups).

The system diagram is as follows:

- [Speaker driver] → Ultrasonic chirp → [Propagates in air, interacts with hand/wrist/objects] → [Mic preamps] → [Sound card ADC] → [Computing unit]
- [IMU] → [Bluetooth] → [Computing unit]

The wide acoustic beam (θ ≈ λ/D ≈ 2 rad, where λ ≈ 2 cm at 17 kHz and D ≈ 1 cm speaker aperture) provides robust field coverage of the wrist region.

## 2. Signal Processing and Feature Extraction

The SonarWatch pipeline performs multiple pre-processing and feature derivation steps from both audio and inertial input streams:

- **Band-pass filtering:** Received signals are filtered to 16.5–20 kHz:
  $$
  X_{bp}(t) = \mathcal{F}^{-1}\{ \mathcal{F}\{ x(t) \} \cdot H_{bp}(f) \}
  $$
  where $H_{bp}(f)$ is unity in-band, zero otherwise.

- **Short-Time Fourier Transform (STFT):** 
  $$
  X[k, n] = \sum_{m=0}^{M-1} w[m]\, x[n+m]\, e^{-j2\pi km / M}
  $$
  with $M=2,\!048$ samples (50% overlap). The sum of spectral energy in $f > 16.5$ kHz forms a key feature.

- **Matched filtering/cross-correlation:**
  $$
  R(\tau) = \int s_{tx}(t)\,s_{rx}(t + \tau)\,dt
  $$
  to amplify gesture-specific echo signatures.

- **Windowing:** 
  - For static gestures, sliding 300 ms windows (stepping 150 ms); for dynamic gestures, 300 ms windows centered on detected IMU acceleration peaks.

- **IMU feature extraction:** Statistical moments (mean, variance, skewness, kurtosis, max) of acceleration and angular rate (all axes) and derived Euler angles are computed for each window, yielding a compact motion descriptor.

The achieved range resolution is $\Delta d \approx v_s / (2B) \approx 0.05$ m, sufficient for gesture discrimination but not detailed pose estimation.

## 3. Sensor Fusion and Classification Pipeline

Sensor fusion is achieved by concatenating spectral and statistical audio features from both microphones with IMU-derived descriptors for each 300 ms analysis window. 

- **Model:** LightGBM (gradient-boosted decision trees, leaf-wise) for multi-class gesture classification, optimized for logarithmic loss:
  $$
  L = -\sum_{i=1}^N \sum_{c=1}^C y_{i,c} \log p_{i,c}
  $$
  where $y_{i,c}$ is ground-truth, $p_{i,c}$ predicted probability for each class.

- **Training:** 12 participants (balanced by gender), ten-fold within-participant cross-validation, yielding robust model fit across inter- and intra-participant variability.

**Performance metrics** include accuracy, precision, recall, and $F_1$; confusion matrices highlight both dominant gesture classes and misclassification statistics.

## 4. Experimental Results and Recognition Performance

Comprehensive evaluation covered 15 explicit gestures, categorized as static (e.g., Wrist-Up, Block Left, Cover Screen), dynamic (e.g., Click Mic, Pinch sides), indirect/fine motor (Pinch, Rotate, Bend), and object/body interactions (e.g., Phone→Watch).

**Recognition rates:**

| Gesture Group                        | Recognition Accuracy (%) |
|--------------------------------------|-------------------------|
| 12-way direct gestures + no-op       | 93.7                    |
| Opposite-side hand gestures (6 cls)  | 95.76                   |
| Indirect (same-side, fine-motor, 5)  | 97.6                    |
| Body/object interactions (3 classes) | 99.1                    |
| Noise environments (17–65 dB SPL)    | 92.6–93.7               |

No statistically significant accuracy degradation was observed across environments (lab, outdoors, mall, restaurant; RM-ANOVA $p=0.516$). In real-time user testing (8 new users, 10 reps/gesture in 30 dB white noise), initial recognition reached 90.5%, with a majority of false activations removable by window-consensus post-filtering.

## 5. Power Consumption and Real-World Deployability

Empirical measurements on production Mi watches indicate SonarWatch drains ≈1.6% of battery over 40 minutes of continuous use (≈0.04%/min), comparable to 24h heart rate monitoring (≈1.5%) and far below continuous music playback (≈6.5% for 40 min), confirming feasibility for always-on use on daily-wear devices. Component power breakdown is additive over idle, sensor, IMU, and BLE loads.

## 6. Limitations, Confounds, and Prospective Enhancements

- **Hardware dependence:** Current design is constrained to devices exposing accessible speaker and dual mic hardware; only left-wrist, screen-up configurations were validated.
- **Gesture taxonomy:** Limited to discrete, explicit gestures. Continuous/proportional input (e.g., 2D cursor via Doppler+Tilt) or daily activity inference was not implemented.
- **Audio configuration:** Single chirp scheme (linear up-down sweep); alternative waveform families (e.g., OFDM, Zadoff-Chu) may improve spatial resolution.
- **Sensor topology:** Two-mic configuration enabled side-discrimination; multi-mic arrays could furnish hand/pose localization. Sensor placement remains a modifiable parameter with future devices.
- **Cross-platform:** Only watch-side scenarios were examined, though the approach generalizes to cross-device interactive regimes (e.g., phone–watch coordination).
- **Noise robustness:** No significant performance loss up to 65 dB SPL; a plausible implication is that SonarWatch would be viable in most consumer or urban settings.
- **Extensibility:** The feature/decision pipeline admits integration of further modalities (e.g., accessibility, XR) and learning frameworks, and supports multi-watch cooperation.

## 7. Significance and Future Research Directions

SonarWatch demonstrates that leveraging standard smartwatch transducers and IMU data with data-driven fusion and machine learning enables accurate, low-power, real-time recognition of a broad set of wrist-centric gestures. The approach bypasses the constraints of capacitive or vision-based interaction in compact form factors, without requiring hardware modification. Potential research trajectories include: hybrid chirp and coded excitation for improved spatial separability, continuous control schemes, integration into multi-device and accessibility frameworks, and large-scale, real-world deployment studies to quantify adaptability across populations and device models [2408.12689].

Source: https://www.emergentmind.com/topics/sonarwatch