arXiv subjects

Calvin Kuo

Publications and source records attributed to Calvin Kuo.

8 recordsLinked to original sources

arXiv2026

Improving the clinical utility of lower-limb surface electromyography (sEMG) by quantifying and correcting for location changes in inter-session recordings

Purpose: Surface electromyography (sEMG) can enable direct muscle activity measurement to support the recovery assessment of individuals with neurological and musculoskeletal disorders. Despite this, its broader adoption of sEMG has been limited given its sensitivity to changes in electrode location across sessions. To address this challenge and enable multi-session sEMG, this work develops a novel high-density sEMG (HDsEMG) algorithm to quantify changes in electrode location and mitigate its effects on common time and frequency domain sEMG features. Methods: 11 healthy participants performed isometric and dynamic exercises with HDsEMG on four lower limb muscles. These were repeated four times, reapplying arrays at shifted locations. The error between spatially-mapped HDsEMG metrics was then minimised to estimate the change in array location, with this compared against ground truth 3D scans. Lastly, relative feature differences across locations were computed at select electrodes to assess the degree to which inter-session sEMG effects were mitigated. Results: Electrode location estimates were improved over the assumption their location remained unchanged in 81.7% of cases, 37.6% identified within 1 cm of the ground truth. Feature differences computed between closest electrodes across locations per ground truth and algorithm estimates were statistically similar. Conversely, feature differences for the same electrode across locations were significantly greater, increasing the mean difference for the isometric max envelope amplitude from 15.9% with the algorithm to 21.1% without. Conclusions: The algorithm's application reduced inter-session feature differences arising from changes in electrode location. This can facilitate more direct cross-session feature comparisons, representing a promising step toward robust sEMG measurement for musculoskeletal and neurological recovery tracking.

eess.SP↗

arXiv2025

Reducing Latency and Noise in PPG-Based SpO2 Measurements: A Kalman Filtering Approach Towards Acute Hypoxia Detection

Photoplethysmography (PPG) is a common tool for monitoring cardiopulmonary health. Relying on absorption or reflectance of light by hemoglobin in the blood, the measured PPG waveform can be analyzed per heart beat using physiological assumptions to extract metrics ranging from heart rate to specific blood oxygenation (SpO2). This has led to the widespread use of PPG for bedside clinical monitoring to wearable consumer health monitoring. However, PPG is notoriously noisy and the measured absorption or reflectance of light is sensitive to factors such as body movement and contact with the skin. To reduce the noise in the PPG-derived SpO2, we developed combined traditional methods of estimating SpO2 from the PPG waveform with a new method to extract changes in SpO2 from the PPG waveform in a Kalman filter, and demonstrated its ability to better estimate SpO2 in humans undergoing controlled hypoxia (down to 14% atmospheric oxygen). The Kalman filter reduced variability in SpO2 to 4.30%SpO2 compared to the beat-to-beat SpO2 variability of 12.59%SpO2. This mirrored current methods of window-averaging the beat-to-beat SpO2, with a 30s window-average reducing SpO2 variability to 4.73%. However, current window-average methods also introduce delays, with 10s and 30s window-averaging introducing delays of 5s and 14s respectively compared to the beat-to-beat SpO2. The Kalman filter reduced this delay to within 3s of the beat-to-beat SpO2, highlighting its ability to reduce noise while maintaining SpO2 dynamics. This capability is particularly useful in reliably detecting clinically meaningful, but transient, hypoxic states, such as those observed during apnea.

q-bio.QM↗

arXiv2024

Characterizing the Effect of Electrode Shift & Sensor Reapplication on Common sEMG Features in Lower Limb Muscles

This study investigates the impact of electrode shift and sensor reapplication on common surface electromyography (sEMG) features in lower limb muscles, factors which have, thus far, precluded clinicians from being able to attribute inter-session changes in sEMG signal properties to physiological changes in patients under the context of stroke recovery monitoring. To explore these inter-session errors, we recruited 12 healthy participants to perform a selection of isometric and dynamic exercises seen within stroke assessment sessions while instrumented with high-density sEMG (HDsEMG) arrays on the gastrocnemius medialis, tibialis anterior, semitendinosus, and tensor fascia latae. Between exercise sets, the electrode arrays were intentionally shifted and reapplied to quantify errors in signal features, using 3D scanning equipment to extract the ground truth shift performed. Results revealed that while frequency-domain features (mean, median, and peak frequency) demonstrated high resilience to the inter-session changes, the time-domain features (integrated EMG and max envelope amplitude) showed a greater, yet predictable, variability. In all, these findings suggest that should we be able to quantify placement shift, this can support direct inter-session feature comparisons, improving the reliability of sEMG-based stroke recovery assessments and offering insights for improving remote stroke rehabilitation technologies.

eess.SP↗

arXiv2021

Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding

This paper uses the reconstruction-computation-quantization (RCQ) paradigm to decode low-density parity-check (LDPC) codes. RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision. For message-passing according to a flooding schedule, the RCQ parameters are designed by discrete density evolution (DDE). Simulation results on an IEEE 802.11 LDPC code show that for 4-bit messages, a flooding MinSum RCQ decoder outperforms table-lookup approaches such as information bottleneck (IB) or Min-IB decoding, with significantly fewer parameters to be stored. Additionally, this paper introduces layer-specific RCQ (LS-RCQ), an extension of RCQ decoding for layered architectures. LS-RCQ uses layer-specific message representations to achieve the best possible FER performance. For LS-RCQ, this paper proposes using layered DDE featuring hierarchical dynamic quantization (HDQ) to design LS-RCQ parameters efficiently. Finally, this paper studies field-programmable gate array (FPGA) implementations of RCQ decoders. Simulation results for a (9472, 8192) quasi-cyclic (QC) LDPC code show that a layered MinSum RCQ decoder with 3-bit messages achieves more than a $10\%$ reduction in LUTs and routed nets and more than a $6\%$ decrease in register usage while maintaining comparable decoding performance, compared to a 5-bit offset MinSum decoder.

eess.SP↗

arXiv2021

FPGA Implementations of Layered MinSum LDPC Decoders Using RCQ Message Passing

Non-uniform message quantization techniques such as reconstruction-computation-quantization (RCQ) improve error-correction performance and decrease hardware complexity of low-density parity-check (LDPC) decoders that use a flooding schedule. Layered MinSum RCQ (L-msRCQ) enables message quantization to be utilized for layered decoders and irregular LDPC codes. We investigate field-programmable gate array (FPGA) implementations of L-msRCQ decoders. Three design methods for message quantization are presented, which we name the Lookup, Broadcast, and Dribble methods. The decoding performance and hardware complexity of these schemes are compared to a layered offset MinSum (OMS) decoder. Simulation results on a (16384, 8192) protograph-based raptor-like (PBRL) LDPC code show that a 4-bit L-msRCQ decoder using the Broadcast method can achieve a 0.03 dB improvement in error-correction performance while using 12% fewer registers than the OMS decoder. A Broadcast-based 3-bit L-msRCQ decoder uses 15% fewer lookup tables, 18% fewer registers, and 13% fewer routed nets than the OMS decoder, but results in a 0.09 dB loss in performance.

eess.SP↗

arXiv2020

A New Open-Access Platform for Measuring and Sharing mTBI Data

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: 1) a centralized, open-source platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and 2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.

cs.LG↗

arXiv2019

Passive Cervical Spine Ligaments Provide Stability during Head Impacts in Vivo

It has long been suggested that neck muscle strength and anticipatory cocontraction can decrease head motions during head impacts. Here, we quantify the relative angular impulse contributions of neck soft tissue to head stabilization using a musculoskeletal model with Hill-type muscles and rate-dependent ligaments. We simulated sagittal extension and lateral flexion mild experimental head impacts performed on 10 subjects with relaxed or cocontracted muscles, and median American football head impacts. We estimated angular impulses from active muscle, passive muscle, and ligaments during head impact acceleration and deceleration phases. During the acceleration phase, active musculature produced resistive angular impulses that were 30% of the impact angular impulse in experimental impacts with cocontracted muscles. This was reduced below 20% in football impacts. During the deceleration phase, active musculature stabilized the head with 50% of the impact angular impulse in experimental impacts with cocontracted muscles. However, passive ligaments provided greater stabilizing angular impulses in football impacts. The redistribution of stabilizing angular impulses results from ligament and muscle dependence on lengthening rate, where ligaments stiffen substantially compared to active muscle at high lengthening rates. Thus, ligaments provide relatively greater deceleration impulses in these impacts, which limits the effectiveness of muscle strengthening or anticipated activations.

q-bio.TO↗

arXiv2015

In vivo evaluation of wearable head impact sensors

Inertial sensors are commonly used to measure human head motion. Some sensors have been validated with dummy or cadaver experiments, but methods to evaluate sensors in vivo are lacking. Here we present an in vivo method using high speed video to evaluate teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6-13g sagittal soccer head impacts. Sensor coupling to the skull is quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (<1mm), while the skin patch and skull cap displaced up to 4mm and 13mm from the ear-canal reference, respectively. We used the mouthguard, which had the least displacement from skull, as the reference to assess 6-degree-of-freedom skin patch and skull cap measurements. Linear and rotational acceleration magnitudes were over-predicted by both the skin patch (with 120% NRMS error for a_mag, 290% for alpha_mag) and the skull cap (320% NRMS error for a_mag, 500% for alpha_mag). Such over-predictions were largely due to out-of-plane motion. To model sensor error, we found that in-plane acceleration peaks from the skin patch in the anterior-posterior direction could be modeled by an underdamped viscoelastic system. In summary, the mouthguard showed tighter skull coupling in vivo than the other sensors. Furthermore, the in vivo methods presented are valuable for investigating skull acceleration sensor technologies.

q-bio.QM↗