The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation ( SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

Abstract-A low-transition test pattern generator, called the low-transition linear feedback shift register (LT-LFSR), is proposed to reduce the average and peak power of a circuit during test by reducing the transitions among patterns. Transitions are reduced in two dimensions: 1) between consecutive patterns (fed to a combinational only circuit) and 2) between consecutive bits (sent to a scan chain in a sequential circuit). LT-LFSR is independent of circuit under test and flexible to be used in both BIST and scan-based BIST architectures. The proposed architecture increases the correlation among the patterns generated by LT-LFSR with negligible impact on test length. The experimental results for the ISCAS'85 and '89 benchmarks confirm up to 77 percent and 49 percent reduction in average and peak power, respectively.

Persons who suffer from intractable seizures are safer if attended when seizures strike. Consequently, there is a need for wearable devices capable of detecting both convulsive and nonconvulsive seizures in everyday life. We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit. Our intent is to develop a wearable system that will detect seizures, alert a caregiver and record the time of seizure in an electronic diary for the patient's physician. Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist-worn device and which in combination produce a very low false positive rate. Stage II looks for a specific pattern created by these three biosignals. For the patients whose seizures cannot be detected by Stage II, Stage III detects seizures using limited-channel electroencephalogram (EEG) monitoring with at most three electrodes. Out of 10 patients, Stage I recognized all 11 seizures from seven patients, Stage II detected all 10 seizures from six patients and Stage III detected all of the seizures of two out of the three patients it analyzed.

Vehicle area networks form the backbone of future intelligent transportation systems.

Abstract-This paper mixes two encoding techniques to reduce test data volume, test pattern delivery time and power dissipation in scan test applications. This is achieved by using Run-Length encoding followed by Huffman encoding. This combination is especially effective when the percentage of don't cares in a test set is high which is a common case in today's large SoCs. Our analysis and experimental results confirm that achieving up to 89% compression ratio and 93% scan-in power reduction is possible for scan testable circuits such as ISCAS89 benchmarks.