Abstract
In wearable and implantable medical-sensor applications, low-energy classification systems are of importance for deriving high-quality inferences locally within the device. Given that sensor instrumentation is typically followed by A-D conversion, this paper presents a system implementation wherein the majority of the computations required for classification are implemented within the ADC. To achieve this, first an algorithmic formulation is presented that combines linear feature extraction and classification into a single matrix transformation. Second, a matrix-multiplying ADC (MMADC) is presented that enables multiplication between an analog input sample and a digital multiplier, with negligible additional energy beyond that required for A-D conversion. Two systems mapped to the MMADC are demonstrated: (1) an ECG-based cardiac arrhythmia detector; and (2) an image-pixel-based facial gender detector. The RMS error over all multiplication performed, normalized to the RMS of ideal multiplication results is 0.018. Further, compared to idealized versions of conventional systems, the energy savings obtained are estimated to be 13 × and 29 ×, respectively, while achieving similar level of performance.
Original language | English (US) |
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Article number | 7366769 |
Pages (from-to) | 825-837 |
Number of pages | 13 |
Journal | IEEE transactions on biomedical circuits and systems |
Volume | 9 |
Issue number | 6 |
DOIs | |
State | Published - Dec 2015 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Biomedical Engineering
Keywords
- ADC
- Embedded sensing
- boosting
- classification