Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes.

Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults

De novo TRIM8 variants impair its protein localization to nuclear bodies and cause developmental delay, epilepsy, and focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is the main pathology underlying steroid-resistant nephrotic syndrome (SRNS) and a leading cause of chronic kidney disease. Monogenic forms of pediatric SRNS are predominantly caused by recessive mutations, while the contribution of de novo variants (DNVs) to this trait is poorly understood. Using exome sequencing (ES) in a proband with FSGS/SRNS, developmental delay, and epilepsy, we discovered a nonsense DNV in TRIM8, which encodes the E3 ubiquitin ligase tripartite motif containing 8. To establish whether TRIM8 variants represent a cause of FSGS, we aggregated exome/genome-sequencing data for 2,501 pediatric FSGS/SRNS-affected individuals and 48,556 control subjects, detecting eight heterozygous TRIM8 truncating variants in affected subjects but none in control subjects (p = 3.28 × 10−11). In all six cases with available parental DNA, we demonstrated de novo inheritance (p = 2.21 × 10−15). Reverse phenotyping revealed neurodevelopmental disease in all eight families. We next analyzed ES from 9,067 individuals with epilepsy, yielding three additional families with truncating TRIM8 variants. Clinical review revealed FSGS in all. All TRIM8 variants cause protein truncation clustering within the last exon between residues 390 and 487 of the 551 amino acid protein, indicating a correlation between this syndrome and loss of the TRIM8 C-terminal region. Wild-type TRIM8 overexpressed in immortalized human podocytes and neuronal cells localized to nuclear bodies, while constructs harboring patient-specific variants mislocalized diffusely to the nucleoplasm. Co-localization studies demonstrated that Gemini and Cajal bodies frequently abut a TRIM8 nuclear body. Truncating TRIM8 DNVs cause a neuro-renal syndrome via aberrant TRIM8 localization, implicating nuclear bodies in FSGS and developmental brain disease

Classification and characterization of postural transitions using instrumented shoes.

The frequency and quality of sit-to-stand and stand-to-sit postural transitions decrease with age and are highly relevant for fall risk assessment. Accurate classification and characterization of these transitions in daily life of older adults are therefore needed. In this study, we propose to use instrumented shoes for postural transition classification as well as transition duration estimation from insole force signals. In the first part, data were collected with 10 older adults and 10 young participants performing transitions in the laboratory while wearing the instrumented shoes, without arm assistance. A wavelet approach was used to transform the insole force data, and candidate events were selected for transition duration estimation. Transition durations were then validated against a model based on force plate reference. Vertical force estimation was also compared to force plate measurement. In the second part, postural transitions were classified in daily life using the instrumented shoes and validated against a highly accurate wearable system. Transition duration was estimated with an error ranging from 10 to 20% while the error for vertical force estimation was 7%. Postural transition classification was achieved with excellent sensitivity and precision exceeding 90%. In conclusion, the instrumented shoes are suitable for classifying and characterizing postural transitions in daily life conditions of healthy older adults. Graphical abstract "Experimental setup showing instrumented shoes, reference force plate, as well as IMUs used for postural transition classification and duration estimation comparison"

Instrumented shoes for activity classification in the elderly.

Quantifying daily physical activity in older adults can provide relevant monitoring and diagnostic information about risk of fall and frailty. In this study, we introduce instrumented shoes capable of recording movement and foot loading data unobtrusively throughout the day. Recorded data were used to devise an activity classification algorithm. Ten elderly persons wore the instrumented shoe system consisting of insoles inside the shoes and inertial measurement units on the shoes, and performed a series of activities of daily life as part of a semi-structured protocol. We hypothesized that foot loading, orientation, and elevation can be used to classify postural transitions, locomotion, and walking type. Additional sensors worn at the right thigh and the trunk were used as reference, along with an event marker. An activity classification algorithm was built based on a decision tree that incorporates rules inspired from movement biomechanics. The algorithm revealed excellent performance with respect to the reference system with an overall accuracy of 97% across all activities. The algorithm was also capable of recognizing all postural transitions and locomotion periods with elevation changes. Furthermore, the algorithm proved to be robust against small changes of tuning parameters. This instrumented shoe system is suitable for daily activity monitoring in elderly persons and can additionally provide gait parameters, which, combined with activity parameters, can supply useful clinical information regarding the mobility of elderly persons

Instrumented shoes for activity classification in the elderly

Quantifying daily physical activity in older adults can provide relevant monitoring and diagnostic information about risk of fall and frailty. In this study, we introduce instrumented shoes capable of recording movement and foot loading data unobtrusively throughout the day. Recorded data were used to devise an activity classification algorithm. Ten elderly persons wore the instrumented shoe system consisting of insoles inside the shoes and inertial measurement units on the shoes, and performed a series of activities of daily life as part of a semi-structured protocol. We hypothesized that foot loading, orientation, and elevation can be used to classify postural transitions, locomotion, and walking type. Additional sensors worn at the right thigh and the trunk were used as reference, along with an event marker. An activity classification algorithm was built based on a decision tree that incorporates rules inspired from movement biomechanics. The algorithm revealed excellent performance with respect to the reference system with an overall accuracy of 97% across all activities. The algorithm was also capable of recognizing all postural transitions and locomotion periods with elevation changes. Furthermore, the algorithm proved to be robust against small changes of tuning parameters. This instrumented shoe system is suitable for daily activity monitoring in elderly persons and can additionally provide gait parameters, which, combined with activity parameters, can supply useful clinical information regarding the mobility of elderly persons. (C) 2015 Elsevier B.V. All rights reserved

Patterns of human activity behavior: From data to information and clinical knowledge

Advances in wearable/mobile device technologies make possible long-Term recording of data in our everyday life contexts. Of particular interest is availability of inertial sensors allowing to monitor daily physical activity behavior, which is thought to include useful information on physiology, age/disease related functional capacity, and quality of life. The challenging task in this interdisciplinary research context is to translate the raw data into interpretable information and knowledge that can be further exploited to provide valid hypothesis, objective evaluation and diagnosis. The aim of this paper is to present a methodological framework that brings together monitoring technology, mathematical tools and modern clinical concepts of physiological complexity, with the aim to reveal and quantify aspects of age-/health-related physical behavior embedded in patterns of everyday life activity