Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Does external walking environment affect gait patterns

The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting

Taking balance measurement out of the laboratory and into the home: discriminatory capability of novel centre of pressure measurement in fallers and non-fallers

We investigated three methods for estimating centre of pressure excursions, as measured using a portable pressure sensor matrix, in order to deploy similar technology into the homes of older adults for longitudinal monitoring of postural control and falls risk. We explored the utility of these three methods as markers of falls risk in a cohort of 120 community dwelling older adults with and without a history of falls (65 fallers, 55 non-fallers). A number of standard quantitative balance parameters were derived using each centre of pressure estimation method. Rank sum tests were used to test for significant differences between fallers and non-fallers while intra-class correlation coefficients were also calculated to determine the reliability of each method. A method based on estimating the changes in the magnitude of pressure exerted on the pressure sensor matrix was found to be the most reliable and discriminative. Our future work will implement this method for home-based balance measurement

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world\u27s population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Health and Wellness Self-Management for Older Adults

This position paper describes our ongoing work in the design of health and wellness applications that support older adults in managing their wellbeing. Specifically, this involves older adults self-reporting on various aspects of their wellbeing and receiving feedback on such, to increase their awareness of their health and to support them in maintaining or returning to a healthy state of being. In designing this application, it was critical to involve older adults, to understand their attitudes towards wellness, as well as assessing the effectiveness of various types of input and feedback visualisations

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines