This repository offers smart-home wearable accelerometer and Radio Signal Strength Indicator (RSSI) data acquired : 1) with low-cost hardware; 2) with high-resolution location annotations; 3) from four UK homes. The data are intended to evaluate RSSI-based indoor localisation methods with activity measurements provided from a user-worn wearable device. A wrist worn accelerometer records activity signatures which are relayed to a number of receiving Access Points (AP) placed throughout the building. Upon reception of a packet, each AP measures the RSSI of the received radio signal and timestamps the accelerometer measurements. Location labels are recorded automatically using a small camera which registers fiducial floor tags as the participant carries out their normal routines in a natural way. Approximately 14 h of annotated wearable measurements are provided. A scripted fingerprint measurement is provided along with several unscripted natural living recordings, where the participant carried out a number of daily household activities which are annotated, where possible, throughout. Codes are provided to access the data and to replicate the ground-truthing procedure.
Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.
Continuous overnight vital signs monitoring would be ideal for patients suffering from epilepsy, where life-threatening hypoxemias can occur during sleep. However, existing physiological monitoring systems suffer from limitations in terms of usability factors and/or limited information of the signals being acquired. The body location of the monitoring system is a crucial consideration, seldom addressed by the wider community. This paper presents a proof-of-concept, neck worn photoplethysmography system, which was developed and tested to assess the feasibility of the neck as a monitoring site for longitudinal sensing of cardiac and respiratory responses during sleep. The novel system was compared against a gold-standard commercial multichannel cardiorespiratory polysomnography system during oxygen desaturation cycles, to assess its ability to measure heart rate, respiratory rate, and peripheral blood oxygen saturation (SpO2) on 15 participants. The findings for heart rate showed a marginal mean error of 0.47 beats/minute with limits of agreement at 95 (%) confidence between -3.17 and 4 bpm. Respiratory rate comparisons had an overall mean error of 0.43 breaths/minute, with limits of agreement at 95 (%) confidence between -2.73 and 3.3 Bpm. Lastly, the system accurately outputs SpO2 with an overall-root-meansquare error of 1.44 (%) between 90-100 (%) SpO2 using a custom calibration method. Moreover, it was observed the neck made it possible for the system to detect desaturation events on average 12.6 seconds prior to the polysomnography system, which used a peripheral fingerbased PPG system. Ultimately, this proof-of-concept study illustrates the viability of neck-based sensing for minimally invasive monitoring of cardiac and respiratory vitals during sleep.
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